{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment 4 Domain Adaptation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task description\n",
    "Given the source domain : photos with labels, and the target domain: sketches without labels, of 7 object classes. Please utilize domain adaptation techniques to train a model that could predict the classes of the target domain sketches correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Requirements\n",
    "\n",
    "\n",
    "* Simple: Just fork the sample code run the source-only part and the DaNN part. (Do Not change the model architecture offered in the sample code.)\n",
    "\n",
    "* Medium: Set proper hyperparameter λ in DaNN algorithm. You might refer to the paper and try adaptive λ. (Do Not change the model architecture offered in the sample code)\n",
    "\n",
    "* Boss： Try VADA (some tools mentioned in this paper have been implemented and you just need to consider how to use them).\n",
    "\n",
    "* Strong: You could freely change the model architecture and hyperparameters in the sample code. You can use all the techniques you've learned in CNN."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T05:07:35.116004Z",
     "iopub.status.busy": "2022-11-26T05:07:35.115257Z",
     "iopub.status.idle": "2022-11-26T05:07:35.119981Z",
     "shell.execute_reply": "2022-11-26T05:07:35.119200Z",
     "shell.execute_reply.started": "2022-11-26T05:07:35.115972Z"
    }
   },
   "source": [
    "### Pre Introduce\n",
    "\n",
    "#### Scenario and Why Domain Adversarial Training\n",
    "Now we have labeled source data and unlabeled target data, where source data might be relavent to the target data. We now want to train a model with source data only and test it on target data.\n",
    "\n",
    "What problem might occur if we do so? After we have learned Anomaly Detection, we now know that if we test the model with an abnormal data that have never appeared in source data, our trained model is likely to result in poor performance since it is not familiar with the abnormal data.\n",
    "\n",
    "For example, we have a model that contains Feature Extractor and Classifier:\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/30eab11bf0b749eaba7b2ca11eabea5e5fc5026d15c54fb99aa9d2db546ad11b)\n",
    "\n",
    "When the model is trained with source data, the feature extractor \n",
    "will extract meaningful features since it is familiar with the distribution of it.It could be seen in the following figure that the blue dots, which is the distribution of source data, has already been clustered into different clusters. Therefore, the Classifier can predict the label based on these clusters.\n",
    "\n",
    "However, when test on the target data, the Feature Extractor will not be able to extract meaningful features that follow the distribution of the source feature distribution, which result in the classifier learned for the source domain will not be able to apply to the target domain.\n",
    "\n",
    "#### Domain Adversarial Training of Nerural Networks (DaNN)\n",
    "\n",
    "Based on the above problems, DaNN approaches build mappings between the source (training-time) and the target (test-time) domains, so that the classifier learned for the source domain can also be applied to the target domain, when composed with the learned mapping between domains.\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/0e35af74164547c491955f30ebe90b418bfe2e917bb04156a7931d18aefebab5)\n",
    "\n",
    "In DaNN, the authors added a Domain Classifier, which is a deep discriminatively-trained classifeir in the training framework to distinguish the data from different domain by the features extracted by the feature extractor. As the training progresses, the approach promotes a domain classifier that discriminates between the source and the target domains and a feature extractor that can extractor features that are discriminative for the main learning task on the source domain and indiscriminate with respect to the shift between the domains. \n",
    "\n",
    "\n",
    "The feature extractor are likely to outperform the domain classifier as its input are generated by the feature extractor and that the task of domain classification and label classification are not conflict.\n",
    "\n",
    "This method leads to the emergence of features that are domain-invariant and on the same feature distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T05:30:24.604547Z",
     "iopub.status.busy": "2022-11-26T05:30:24.604020Z",
     "iopub.status.idle": "2022-11-26T05:30:24.608142Z",
     "shell.execute_reply": "2022-11-26T05:30:24.607383Z",
     "shell.execute_reply.started": "2022-11-26T05:30:24.604516Z"
    }
   },
   "source": [
    "### Import Some Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\anaconda3\\envs\\paddle_cuda\\lib\\site-packages\\paddle\\utils\\cpp_extension\\extension_utils.py:711: UserWarning: No ccache found. Please be aware that recompiling all source files may be required. You can download and install ccache from: https://github.com/ccache/ccache/blob/master/doc/INSTALL.md\n",
      "  warnings.warn(warning_message)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import paddle\n",
    "import paddle.nn as nn\n",
    "import paddle.nn.functional as F\n",
    "import paddle.optimizer as optim\n",
    "from paddle.io import Dataset, DataLoader\n",
    "from paddle.vision.datasets import DatasetFolder\n",
    "from paddle.nn import Sequential, Conv2D, BatchNorm1D, BatchNorm2D, ReLU, MaxPool2D, Linear\n",
    "from paddle.vision.transforms import Compose, Grayscale, Transpose, RandomHorizontalFlip, RandomRotation, Resize, ToTensor\n",
    "from paddle.utils import try_import\n",
    "from paddle.vision import get_image_backend\n",
    "\n",
    "import cv2\n",
    "import PIL\n",
    "import PIL.ImageOps\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import matplotlib.colors as col\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T05:29:21.000907Z",
     "iopub.status.busy": "2022-11-26T05:29:21.000384Z",
     "iopub.status.idle": "2022-11-26T05:29:21.004582Z",
     "shell.execute_reply": "2022-11-26T05:29:21.003904Z",
     "shell.execute_reply.started": "2022-11-26T05:29:21.000877Z"
    }
   },
   "source": [
    "### Preparing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "if not os.path.exists(\"./work/PACS\"):\n",
    "    !unzip -d ./work/ ./data/data333036/PACS.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x1800 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def no_axis_show(img, title='', cmap=None):\n",
    "  # imshow, 缩放模式为nearest。\n",
    "  fig = plt.imshow(img, interpolation='nearest', cmap=cmap)\n",
    "  # 不要显示axis。\n",
    "  fig.axes.get_xaxis().set_visible(False)\n",
    "  fig.axes.get_yaxis().set_visible(False)\n",
    "  plt.title(title)\n",
    "\n",
    "titles = ['dog', 'elephant', 'giraffe', 'guitar', 'horse', 'house', 'person', 'dog']\n",
    "plt.figure(figsize=(18, 18))\n",
    "for i in range(7):\n",
    "  plt.subplot(1, 10, i+1)\n",
    "  fig = no_axis_show(plt.imread(f'work/PACS/src/{i}/{0}.jpg'), title=titles[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1800 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 18))\n",
    "for i in range(7):\n",
    "  plt.subplot(1, 10, i+1)\n",
    "  fig = no_axis_show(plt.imread(f'work/PACS/val/{i}/{0}.png'), title=titles[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T05:34:48.272488Z",
     "iopub.status.busy": "2022-11-26T05:34:48.271337Z",
     "iopub.status.idle": "2022-11-26T05:34:48.278997Z",
     "shell.execute_reply": "2022-11-26T05:34:48.278055Z",
     "shell.execute_reply.started": "2022-11-26T05:34:48.272454Z"
    }
   },
   "source": [
    "### Special Domain Knowledge - Canny Edge Detection\n",
    "\n",
    "When we graffiti, we usually draw the outline only, therefore we can perform edge detection processing on the source data to make it more similar to the target data.\n",
    "\n",
    "The implementation of Canny Edge Detection is as follow.\n",
    "The algorithm will not be describe thoroughly here.  If you are interested, please refer to the wiki or [here](https://medium.com/@pomelyu5199/canny-edge-detector-%E5%AF%A6%E4%BD%9C-opencv-f7d1a0a57d19).\n",
    "\n",
    "We only need two parameters to implement Canny Edge Detection with CV2:  `low_threshold` and `high_threshold`.\n",
    "\n",
    "```cv2.Canny(image, low_threshold, high_threshold)```\n",
    "\n",
    "Simply put, when the edge value exceeds the high_threshold, we determine it as an edge. If the edge value is only above low_threshold, we will then determine whether it is an edge or not.\n",
    "\n",
    "Let's implement it on the source data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OnkJeM/tTGB8fL9brPe95T/Hf8p5j3huUeNe73lV8TeR8yMc+9rHidpGzkfpnseUoMpU9hdzXyn30d77znWIOvOhFLyruf8MNNwT2Ewk8ft6XkPsF2V8+J2cC6Udp1wc+8AG1TeS1UsaHP/zhs/b/zGc+U3ztscceK/5bJKvSnzbE9uLGG288a/u2bdv85z3veTNSfzLzLOb8LfGRj3ykWP4tt9xSd/n78Y9/vHiO0t+1114b+Nxn/s4dCzFXprKnkPElf2LzInYlcqx8Py2VLdYlnZ2d/pve9KbA8TKv1NHREdguVi0XX3xxYD+xVMF7RDnv1q1bi31kfvfcuHGj/+xnP1ttkzkiOfaVr3zlWfNKpl2MSTX1fs1rXlO0dakEmQOQ7+p/8Ad/UHf39idPngy8l8j4kHrV8709VxpPE1nSLk84l1V68mtXCVl6L8vzZRm9/DpTQn51sT1oB5GnyYr8xpSsyC9ZlSK/1OAvVyI1kF8bUMIg9SgZY8uvo/IwAilXfqm47777Ki6LkHrIQ3lCquQhPhRDpEliG2ND5DXyy71JacVAacW+PHBAlASYEyLREWmNPMG09ORkqYM8tErqgCb2JiWTfXnQh02qVJKZyC+p8quuCVptYF2lnvKrpbRL8lz+7eJb3/pW8ZdLUSDIMaU/+eVYoEUNsVH6LKvmwQnVjFHJR8m1EiL1E9me7anjIhfDXJDjJAdLcjtZqSMr+OWpxyhLE0saWZVSeviFfO5JDuNKjRKyGkNWEsnDVSSv5aEjslJEVoz8z//5P9V+padz2x5mK+qA2X56t5zfVTbWb6p64j4lSv0i143UP4stR6tBJMzymfqSl7ykuPJLpJlyTy4rq1DBU2k+TQdZFSTfEaSNolDEsoVK89n1EGzX+470IXO5flns+XvrrbcWlYLy2Vu6H62n/H3lK19ZtIeQh9RK/prnY/7OHQsxV8ohSnOpg/zJd1t5kNlVV11VzINS2TI2ZRWwjFP8bifzPZdffnngu52oSuWBbLL6vYR8h5WxK99vS6vyZfWtjHW57y2dT+oidiuSrzJ/hMiDHc0+l3Eu9pFoeYFUU2+pRyXvJbLaWVY9S/nyILl6u7fv6uoqtltWpYuyWZQVpu1Ovd3bc9J4moidgwxMmx+NTMpIMqF/UiVvDKU3GXlTMAdYpTfNsmze9HGVY82EFXmN+JrIoBJ/NXkzEhl+uQknQuoN+QImb4hmzgi2beVyUSZz5YnmkhOSc5ITImkyc0I+bERaI7ISQSatT506VbyZLYfcoIhUR26M5UNCPpzlwwu9hOVDXCa/p/JqFJmaeBbJJLVMRktdRXYllMthuQkQiVXpBqT0J5K+Un8SYlKSe7q8waY7RtGLq9znlm3f0mcj7is5Ku8LIosT5CnncpNsy9HSjz9TIVI5uYmVfDe/iNj8wFOpVOCLymwg53eVjfWbqp64j9kv5v0EqU+Yo9Xxzne+s/hfM5+ryZFqkS/c4j0o10i+8KPXcTU5Kv9FL1VzX1s9pQ+Zy/XLYs7fxx57rOgFK8/kEevEesxfsTaQvpbJLZnwk8Vi8u/ShAzzd+5YyLliQ76TyiSj/Ml3Rpljku9qOE5K9gryg4v5/U4WOOJ3O5lQlclsmSgujS1ZUFR6PheeTzyZzfNJjso4N/vN/G4tE5liNyH2qCtWrChaXYgXtdgy1FLvUl3LIRP2N954Y9HeQnyHcTFZvdzbx+Px4liUewGx4hCLFLGllDmIer23167OZE6Y7S+PJVyrmTHRvva1rxX9eeRXlne9611FY2w57uMf/3jglydCFksuygSw+BrJh5r4LoliQB5EJR/QsrIAEe9v+QCUPJL95b/isWaa85vIG7R8iMnKCPmFUX7dlIfgiWeWbDMfluNCclR+6ZXVwn/9139d9OaSD6H/+I//KHoTm7/+IvKaPGBPjrMh5yLERG4k5ebL9PKeqTFayedWNfvKShHx7ZLclBt2+a+UL6sMSsgPM5KTrhUQNqQdcsNfovQAlpMnT56VO7Kt5D82W0j5shLD/EIpZQulG2asp4lsk74wVyqU+sXmL0nqD+ZodZTyVVZDlpA8EY9AEzOfakEmiWSlpPieyme/TJAhpRx05SiWL/WUL8fyhVru37EMWTFlq6f0oTwjhdQnizV/ZXGV+KiKik/qXunq0bnOXxPxvP3iF79YXHEp3wmYv3PHQsyVckgZ+P1Sxpu05c1vfnNxBbNQaoP4A8v3URN8mJv0nayIlpX6MnEu3z9lIZRM8JYonU+erbVr1y5rvczvrLbv1vJ8Hnl+jjxjSD73ZJJU5prEL1m8iauptyxwnOpeQBQIMvkqP+yYioV6vbe/8sori+VJnWUiuR7v7TlpPE3kVxAx0MZBhr+ayq84MtDkAVnV/JJZMmHHX2zkQ2Qmb5pl4kp+Jf3Xf/3XwGC0SeIJqWfkhkt+hZWcMbFtcyG2EHIe+VDDN1iZNLZ9gItkR56eKx+y8mFYif1MCVnNLH8ihZEJaZEufeMb3/De+MY3Fp8wLXWQG2HXamOZcJZfFeVmAX/lrsRaQs4vD22QGyiuWiDVIDczX/jCF4pP7ZUHwJRjOmN0ppCbc3mqtdw4SZ7JgzZQsSM3o5IP8vDaShF5onz2lyjdTN9zzz2Bm8gTJ04Un3CMT3GfDaR8WfUhT5tHy53Sg3hL9Vu9enWx3lJPE3mgie1LgfSL3FRie0l9wxytnJLU2Mxnab/In/FhWmY+VYt8MZa2igWdfFEX1ZGJfGeQH3RtOSrlyz17aUIN33fkCfEl5N9SlllPsZ+TyTn5YZzUL4stf+W7rUwYSzskN0oTIPWWvzZKK4xLqy2Zv3PLQsuVapA8kYe5i2pVJnzl+6TkWek78VQLmEoWFWKFKnNYsuJY5rNkcrdE6XySR5WcrxxyLlEGyJ+sLJbxLYulZPK8mnrLRLlMrErOyY9MJrIIUr6zf/rTny4qAqphvu/tU6lUYOV2vd3b055imsgEkXzYicQMn+4oMnV5Q5Dl7uYTXKdCJnLkQ1Yk8cjf//3fezNJaXILfwmTgShvvoQ0EqVfYGXiVt7MccJYJDHVnEcmUeXX/xKS13JeGyIrkh9y5Jde8SKq5Am4sr/5S3XpDb0kLRH7CtlHbgZMSsfa8lc+bGwT3Cbyy7asxJAVErabYJHPEmJD/DdF2ic/bsjnnG01x9/+7d9Oe4zOFHLTKDn99re/vXhDaMtR+bJhu9kS+ykTWZUiksLrrrtObTvvvPOKN7Ly5QXfO+QzXMqW1UiziVjciCJC1BElpM8/97nPFW8m8cni8t4iKzDQNku+qMtT6UWuaCJtnerLGKkvFlOOVopMIJnSTWnzRz/6UbVqq4Tkq+Sx5HMJOVb6ROSrtSpx5Lkk8sVc8lRWG7uQ8mWhCbZXvtTLqizMUVlBJT8qm98V5N/y5V8mJBCR6sqXUnw/IPXHYspfudeUCVO5H5XPVtcq2vnOX9u9QMlnWdq+e/fuQPnM37lhIeZKtZ8pMlZKnr2SBzLn9LGPfcz6zBxzHMv9oPSLPJ9HrClkEh6fySMro2VC91Of+tRZfru289kQC9eSRUIJOaf8eFLK6WrqLe8lcg3l3tREVkRLXWXltPSxi/m8tx8fHy/2iW3RmswPyHPF6vXeniuNZwD50BKPGZkgll9sZML385//fDEZxLelWkTyLoNdfoGRXxRlAMuqQJn8kl8FZmploLw5yCpj8ZCSDyf51UEGovyaYXtzIKSe+dCHPlT0PpIHA7zlLW8pvsHLDy0i/xQz/0qQPBDZkuScrCIW2Zj4DIkvsshJTURWI+cvPVgObxxdiI+4fABI3skHp/hxyeStfGCWVhs84xnPKE5I/5//83+Kv8hKfWTlgdhnyGt/+qd/WvyxSqRN8qtwadJaziO/1NokKoicW1Y6yQML5Fd26TPpL1FHyHZZ5YwfXISUkDErP4jKCgUZ87JyQnJAJJW/+tWvirkgtkfCdMboTCG/okv+SL3Ew878Ila6MRNZnNxclXy9BbkhkxyXXJAVDfIwzJtuuqn4pbPkg4c3q/J5LW0WLzWRTMr7j3yZkX7CH6FEQSQecaJSKIe855Rkh/IDmHzBKX1JvvDCC9WKEHmGgcj/pA5yw33ppZcWf+iS9wtZkYHqB6m39IW8j8h9hlwTOU5WR/3hH/5hoHx5/5M6vPWtb62y18l8sphyVCjlhPj0C7KfPIRaeP/731/8r+SufGGXP/k8L3lLiselrBbCz26ZWJIvWe9973uLOSD7y+e25K5MEpn3HfLjrnyOPv3pT3e2UVY9yee+fEmTL/myugqR+4HSl3X5HiHXQPpBHnYkXxrlvkS+G5Q8XEsyYHmAj+Sn1Fe+eEvOy7lFwWSqlOR7ipT97Gc/e8prQuaPxZS/orCTlXBi0Sar6eQPZe9in1gP+Sv5JGVJO2WVqqgAZZJHJodl4g6fncL8nTsWYq5Ug1g1yH2bfLZI7kgfyISmfMeTnJB7USlTbCfkeVXyXQ8XIErb5V5Qxqd8F5V+RGTlvKx0FZ9jmUCVsmSyUn7kkZyR762ygrsckvOyGFIWK8n8ksyRSe7KJL/UT5DzVFpvmWuTdov3MFpPyDnlRwT54Un6wfyMlbyRHJzve/u9e/cWF7lJX8uEtPSx/MAk9d2wYUNgsrvu7u19MiPcd999/nOf+1y/tbXVb25u9p/xjGf4v/rVr9TrX/7yl+WnLf/uu+8+69jSawcPHlTbcrmc/4EPfMDv6enxm5qa/Gc+85n+o48+6nd3d/t/8id/ovb7+c9/XjxW/lviaU97mn/eeeedVc7rXvc6f/369erfhULB/9jHPlbclkgk/Isuusj/wQ9+cNZ+gpTxwQ9+cJq9RMjscssttxTHcTwe9zdv3uz/wz/8g//Od77TTyaTZ43nt771rdZzfOlLX/K3bt1azInt27cX81PGvuvt8q/+6q+Kr0kuVfpe8cpXvtJft25dsYzly5f7L3jBC/x77rknsJ+8B3zyk58s1kHas2zZMv95z3uef++996p9vv/97/s7d+4stm/Dhg3+Jz7xCf+mm2466/1E3hPkD8lkMsX95b1C6rFkyRL/4osv9v/yL//SHx4erqgtZPHyxBNP+G9605uK407GZ1tbm3/VVVf5f/d3f+enUqmqx6h85jz/+c8/qxxz7Lo+S22fhSW++c1vFl/74z/+Y2tb0um0v3TpUv8jH/lIYPv73vc+f9euXX5HR4cfi8WKOfuWt7zF7+3ttZ7nu9/9bnF/yac1a9b473//+4t5hjz88MPFurznPe/xp6LUVtuffE4j+XxefZ7L9ZC8/trXvmY97yOPPOI/5znPKd6rdHZ2+q9+9autbfrsZz9b3GdkZGTKupL6YzHkqODKEfzMPnDggP/yl7+82D5pp4xr+bz73Oc+V7wXNpmcnPT/4i/+ongPLvl86aWX+jfffPNZ+8n9RSgUKt6fl0PytVw9sZ+Fo0eP+i972cv89vb24vcKuUfYu3ev9dxf+MIX/HPOOUfd9/zN3/yNtU2XX365/5rXvKZsPUn9sBjyV+rkygn8Hjrf+fvjH/+4mIOrVq0q3guUroX0la185u/cspByxfX50dLSYn1t//79fiQSCdwTSrkyJyX3rtJWGVevf/3rz/qeKXzxi18s1kf6TPLGxv333++/5CUvKc5BST5J/9xwww3F79wlSt+T+/v7A8cODAwUv2/Ld1lpg9RJxrL0g0ml9X7b297mb9myJbCtVL7rD6/FfN7b9/f3F699qT9kvMqcw5//+Z+f1Xf1dm/PSeMGYnBwsDggP/rRj853VQhpGK6//vqzPlxmkk9/+tPFm87Dhw/PWhmEkNr53ve+V/zsvPXWW537fPjDH/Y3btxY/LFmNvnMZz5TvFF03ZzWE3KTLDeyhCymHK0GmYySyaF6R770y32K/JeQmYb5O7swfxdXrpDyyES5TPb+9Kc/9euFzyyCe/uQ/F91a5PJXCDSG/MJlCUZjcjuZKk+IaR83ogMRCQ1Ihex+fdOF3n7FJm4SGXm8mEKhJDqrJhEuicWDy57J5FyyUNq5CnaIpmdLUSGKvI58W6rZ26++eaiX5v47uGT3QlZ6DlaKeKzKhJasb9CmWo9IrJasbgS+ylCZhrm7+zC/F1cuUKmRmwopQ/FtqUeePkiuLfnpHGdIn4o8icep+LrJBPFYlQuniriN0oIsT9NVvyr5Mb08OHDRY8k8Ra///77nQ/YqAUxshevUZkolsloeRAmn2hMSH3xjW98o+jb9fGPf7z4MJS3ve1t810lQgjAHCWkcWH+ElIZzBXS6HDSuE4RU24x9JZfQOXXUDHvlicjygM/ZBKZEHI2YvYuE7m9vb1eIpEoPnRGfvWr5AF11VAyvJcHGchDL+ShFYSQ+kJWccjnpTxwQh7yKg/gIITUD8xRQhoX5i8hlcFcIY0OJ40JIYQQQgghhBBCCCGEKMI6JIQQQgghhBBCCCGEELLY4aQxIYQQQgghhBBCCCGEEEVFhiryxM4TJ054bW1tfNIjIYC4u4yOjnqrVq3ywuH6+w2GuUuIHeYuIY1LPecvc5eQxsxdgflLiB3mLiGLN3crmjSWBFy7dm1NBRCyGDh69Ki3Zs0ar95g7hJSHuYuIY1LPeYvc5eQxsxdgflLSHmYu4QsvtytaNJYfrERdm3o8SLhsNfZ1aVea2lu0SeDZ+qFjEnsfCGv4lw+q+J4PKaPj0b0/nm9P/5aFJwdD/6KFIvpczW1JFVcKEC94BDfL8B59QvSRnWsHywjl8tbYzwXPlsQ645PyoxEdFtzuVygjHxe/zsSiVrPhcdHI9AnUA/EfNwh9kPwCPsvcz70IXRVsB6x4EXH+sbjcRU3NTXBiSPWdsuvhSVyWT1eJifGjDJ0mdGojlub9fVPwLiIRnWcy+kyJtIZXSXj10ns3lhUvxbKF7xUJut9/KbvqxypN0r1Wr58eTF3uru71WstLS3Wa2X+OovXIgvXIpFIWMc2jmfMV4zNMjB3m5ubrWXjMZhjeF4cj/geYv4b21EJledu3rqfM3fhvK5nkmIfmO01X6sGzMlyT/HFayNP/rWB/YB1ymR0Xk1OTgaOcfUJloFlY4zXL51OO+vu6mupo9TtK1/5St3nbonh4WEVd3R0zEONyGKilvE2H2O0HvO3HutESL1Rr3lSr/UipF6o1xwp1Usmxtrb2+e7OoTUDSMjI8UfVKaTuxVNGpe+3EdCYS8aCgeMkPPw5T0KX+pDoeAESCKui0qGpy42AudyTTyFjYmn5lY9CeZ7uvycn7NOzMRgAjGGkyaOySlzP5xIzeayFUxye9bz+jgLWyxDT8a55BU44ROYpIZJ7nITgfhvnHbC5gYm5nDyG2ZRcaIfJ96ffE33VTyB1xMn9nQZERgXONmUh76NxYJjB+uVSOg+aU7qPkxCX+GkXgEuTVNS7xMxJtDCMDHtF3RdCn5W/cBQrzKYUr3kv2YdsY9xPJn74WsYu8B9XBPFZl7gm1ggNyDGyUjXZCJiloH74YQ3TkBWm7vmPjjmXWPCVV8X5v6uyXPXBDLWEeuHcbl2YF8F3jcc1wMncXEy2ZyYxnNhGa4Ycxfr6/rxwtwP6yL9hrlRj5j14g0wmQ1cP1bVcvx85FI95m891omQeqNe86Re60VIvVCvOVKql9wv856ZkJnN3fozpCGEEEIIIYQQQgghhBAyb3DSmBBCCCGEEEIIIYQQQkh19hSKQqFo+oC2DOhnkM1quXskYvjCRmJWOWPQE9Tl/2tfSh0y7BACXqpgoeDyAA1YFYD9abmV2zGw2cgVclP6uGJ56K9ZTtLp8mV1eYWG0TvV9YuAIUFF+w70Yw54HaOfLJSB1xI9l02w7rk8tD2bs3oSZzM5q/8p1ilq2FNge5NJ7WPsQ+snJlPWbgiBtYaHdiqmXBf6p5CF8ZMrFP8agdK1xGuClgI4nkyJv8vv1uULjOfFsYyYdgiV+CAjrrwoJ7vAdrj8jV25V4n3cLnjXdYRlfSPeazLvsNlHeE6r6tss+6BPIbrhMfj+JmYmKi6vIDPOYDvA9hWl190OY9n05u7Wl9rQhoB1/uTy9bGtU8tZRBCCCGEEEIWFlxpTAghhBBCCCGEEEIIIUTBSWNCCCGEEEIIIYQQQgghtdlTiJOAqI198HIAF4iAjUAEpf9nWVLkrQYDIYjBESBwbBTOG8HCi+XbpdpuCTdKLHWBQeVlUIZZKNil8y75ukuSjfuYCvx8PmutL7YdbR2wwiGUtcM5c4YUO2gBMLW8G9vnilMpbQNx9jWftEvvYZ90Km3tt1g85uyrRCKh90toe4oCWKWEwnBt8HrieEULDLSqEOn9hK47HhSLRr18YAzVL9Ln8hccd2HrODPHLB7jkvS78g1jtK1wWV6UOxduR8sEbEc52bSr7VgvHP+uOpbLb6xXMHejU7bP1Q6zz132Ha7cxfa5rEPM3EVc9h14DMau8kyrEbSTwTzG8iq5tlgnswy0t0Di8bh1OyGNhpkXLouJcpZY9fgU9dmqLyGEEEIIIaRyuNKYEEIIIYQQQgghhBBCiIKTxoQQQgghhBBCCCGEEEJqs6coEYvpw+JgHVAATwlTTogKSrShCMTgEYCCSx9k12E4byhkyrFBqp3LqTgPdhp5OBdaPATjkLMd+TwcD7YOKJFGWSVKxtEaAQ61yEuxLkYT9cnwAOu5/ILuA/M0wTLtvx2I/YKtfdim8fFx6/Ynq+WqPFwPuE44rqKxuNU+IxoL2gG0tLTo8mC/UEjL3SPQn1herqAtLKIo+TdU8Pm8PiZIIWAl0gigXQBK9PHanZ27U8uEKxn/lUqM0eLBFVdiy2BaFbiOd9kbuM7rspcwX6tWUl1pv5Wz4LC1yZW76XTa2je2vrPthzYSOJZclhSmlQfmLr7m6kMsOwd57KqruV+5thDSSNRi3TCd9yNCCCGEEELI4oQrjQkhhBBCCCGEEEIIIYQoOGlMCCGEEEIIIYQQQgghRMFJY0IIIYQQQgghhBBCCCG1eRpHIlEvEgl7cfCszOe0R2YWvF/j4E9bPDYatnv7+uBfCRai6az2mx2b1C/sPGeHirdt17HQ3Nykjx8bVXHviWMqTk1M6PPCPumU3p7Poddl0NfUB9/mLNTRB9dgvwDtg3l59DGOgl+wOXXvo2cq9JUPfYXHhMC72Ieq52F/0/YTPUSjUR3ncrq8HHh+xqBNGfDxRc9Q0zERvaSRaAQ8sZNx65iJRGLW65psSgbOhX6m6MGYD+Xt1wN8s+NQRhLG9OSY9mk2fWAD5RnW0vWM+MxK3dF7Fq8dxrhPuT5Gj0zsI/S6nYB82759u4p37DBzt9l6zLFjx6z+2aOjOndTqZS1HSZY90wmM+U+Lg9Q9Oyt1CfUdV6Xj7FrzJ2du1Fr2wNe6o5243Uycb2G5SWTSWufYP1aW1tV3NSk89hsVyX9gzGO0UQiYR0XU/UjmX9/22p9dsn89NtclFfOH57UDnOXkMaF+UtIYzJbOcbnTZD5gt+iCSGEEEIIIYQQQgghhCg4aUwIIYQQQgghhBBCCCGkNnsKWRIvf3mwLUC5cjSk47yxfN7P479DVruAHCqi4y0qvPa516l415VXqzgC+wgtIH/uaNdy9wT4QsTCuh6T42MqHhvRsuYsSLjzGS19Lx4zqY+ZGNcy+nGwNEindDw+Oqji0/0nVTw4eBrOo88phKB/Q9CPMehrtLDIR/TcfzYE+8P2GFgxmBTAcgOvJ8q5I3G9PQ3tLifnDznk4GGwwwhDHUNwnTLZtIqbQ0FZO4Jye7QpQHl9NKTLaAJ7i4ANg8NeoawFRj7vFeBa1TNT5i7EKOkvZ/mANgR4HdCq4Prrr1fxVVdd5bTAQHuKjo4OaxkYo4XF8PCwtR6mBQUeMzY2Zo1xDI2MjKj41KlTKj5z5oz12HK2EAE7Ggd4LO6P/VnpMdhXeDzWt1zuuqwcXGXg/uVsL5B0Oj1l7uJ50d4CLSmwHeZYdeWu7FfOymSxUonsbSYld3Mts6Mkd36uG/t99mHuEtK4MH8JaUwqGdszmW9znUu0wyAluNKYEEIIIYQQQgghhBBCiIKTxoQQQgghhBBCCCGEEEJqs6co+E/+4dL4gJQd7AXMxfP5PMi2C1oin4U925euVvHv3/AqFe/YtUuXh5YJYS2PFpIxLV+ORhJWa4V8QUuSo+2tKl7RvU5vh/aFQ6ZUX0uqC3l9rgxInbFPEjEoO6vl8uMj2p7i1CltWyHseWSPih+4/z4Vn+jT+zVB09uadVvjUS1Fx0uT9wwbBbSkQAsAOB7tJdKptFP2XyIGbX2yfF1GOHCttNQhDH2NCggsY3Bw0GlrkIV+96E8LDse0fVqBnuKAo5J6A9zXOU9KAMqGYlGvVAkOD7qlVJ/OHO3DGhpge3Ha7Rs2TIVv+pVOncvuugiFWPZpv1BMpm02ing9cb6trW1qbirq8t6XlPCg3YE2CZsRyB3wQIB90Hbit7e3kAZDzzwgIrvvvtu637YvtbW1iktLMrZPeC5XHYRk5OT1na4znN27tp/W8T+dY0LtPIwcxevB5bnstxAewq8foirrmYdZT/KJctLz+aif+pVZsexUb7fqpVjuqxwylnkkKlh7p4NxxFpFJi/Z8P8JY2Aa5zOhZVDvVrL0MZi4cOVxoQQQgghhBBCCCGEEEIUnDQmhBBCCCGEEEIIIYQQUps9hSw8l9XnoOr3ogE5uH4hY0qqC1o6XciBtLhJS8Cf+/IbVLxt98Uq7u3XEufl3VoG37NiRaCISETXJQV2CtksWErA6vloTMulc9goaBPaJzx5jJZIZ3wt+57M6Did1bLreEEf35LUkvruVVpS37NxR6CMnZc9Q8XPG9RtP3Bgv4rvv/ceFT+x5yEV953pU3EspBubjAatCFqbdNvDYCuSBWl5COTuLrk6yhZyeF2fPMr4d+l4fc09OD6f12PG99E+oOCUpeP1KTjksNmCPu/E+IS1fiGwzIgY5iqxOFp++AFJv3+WEUt9Iv0hfy5LCrymLgsD01IA7RtuvPFGFe/evVvFfX16PC5fvlzFK1euDJwXrRVSqZS1LnhN0erAZVVgSmrQgsFlp5BOp63nbW5uVvHq1dpGZ9OmTYEyLr/8chXfcIN+P9u7d6+K77zzThU/8sgjKj59+rT1epjWES0tLdb9sB3Yn9gOd+7q61oO1/FYBo4xV3+ax7uk7GjNMTExMaWs3bzmOEZN24tyVhYLnUpksQvROmC2ZHa19E+99+9061RJftdju+sd5u785y4htcL8LQ/zlzSyJYUrjxuZ2bKWWSj9s5hYvN+aCSGEEEIIIYQQQgghhJwFJ40JIYQQQgghhBBCCCGE1GZPkcvmPT/sB2wI4ig7BIlz1JiPzsOy9SzEz3z2dSruXqFl3//yjW+qeMO6dSpev0bvc7L3ZKCMRx/bo+KBwUEVN7c0WeXkq1du1PWN6q7IgzVCAmTeTzZSh4mEPu+SsD4+EtXH4Op7tIHA3vELhrwejlnepe04Vi7tUfGlu7UMfnhiXMUHDx/S8UEtib/jZz8LFPHIY4+quL1V12tlh7bQiIS1nDsSiVYtZce2R6FPsB8KIFnPgq2HGEZUInkIyBsgDtgwQJga1zYisZiuUygSuFCBMqJhvV8ymYTjY54XaozfXUTmL3J8tDBAiwe8pqZs32Vpcd11Ond7evTY/OpXv6riDRs2qHj9+vUqPn78eOBce/bssdo0oBXDli1bVLx27Vpr7uJ4wO3ma2hb0NnZOeW5XFYGLmsMYenSpdb+ueKKK1Q8Ojqq4gMHDqh43759Kv7xj38cOO+jj+rcbW1ttZaHOYP2FuXqi2Db0eoC+wHPhTYSSDkLCJdk3VXHsbEx69jFMrCu5r8xd+V4c9+FTrWy2Ome11VGIzBbMrtyVioLmcXU1tmAuVtfuUvITOfvTJ53JsuYa5i/pNEsKWbyvDNZxlwzW9YyjdYPC5nGmPEihBBCCCGEEEIIIYQQMidw0pgQQgghhBBCCCGEEEJIbfYUfqFQdE5AKXI+r6XPkYBUP7icPAuS43N3XaTireftVPH3vv99FZ+/4zwVX7p7l4ofvP9eFd9++x2BMnpP9ao4HNdNO+ecbSq+6PwLVIwr43N5LcGPxcCqwpDmowg7FopYjwmBtYIP3gi4veDp/ujrHwiU8dsnHldxf1+/irs7l6j4Auif9jZtKXHB9nNUfPH5ep8rn3J1oIzv3/wfKv76P3xJt8PX13Bpl7YGCIN9A8rd0brAlKLn8zhOdHvzYIXgkqK7ZO2mNQbuFwa5eQzsMKJwncJwDQI2FCEYr4blBCojcOyLtKJSq475pnSdsL4Yl5Pq4zW+6CKdu+eff76Kv/Wtb6n4wgsvtFox3HPPPSr+xS9+ESjjxIkT1vG1Y8cOa9koa8ExFLCZKWPF4LJvqFYq09fXF3jtt7/9rYp7e/X7UVdXl7V/Ojo6rP25e/duFV9zzTWBMr73ve+p+LOf/aw1F7q7u611rzx389b9XOMHcY0l08IC98MYryHWqxJpv7kdr5WZuy5LjcVApf1XiTxstuR7s00t9Ztuv7mesl3tPmTxwtyt39wlZCoqGTuV2s4wf5m/pL7GbaXjbLYsa2abWurnamulfTBdGy4yc3ClMSGEEEIIIYQQQgghhBAFJ40JIYQQQgghhBBCCCGE1GZPEY/HvGg4HLB1SKczKg6j3UMhKF1u7Vqm4gsu0ZL1W39zt4p3gm3FBSBL//cfaCuFX//mThUPj4wGysimUiruWtqu4uc88+l6e1uniidzWp4cjSVUHEJ7AmMlfMHXUu0syLPjUZBRO6biJyfHdQx17R8I2lNEwOqibYmWr6cLWjK+/+gh3aZO3abmRFxvX6Il6ks79XmEG65/iYpHh3Rdfv6jr6t4eUxL6sPQEUFZN9pWBIeTD32FcTqTmVJyLlYoqmzcHijB8zJwrqA9ha5LIQTWI2G9TwGsQ8Ke3p7PGbYG0O8R49pmGkTinkgkzrIgSMEYREsA03YArRUuu+wyFd96660qvvjii1W8c6e2nPnOd76j4ttvv13FQ0NDzrosX75cxdddd52KO2GcYx3RcqFSORAe77JDQCYnJ62xaU+B51qyZInV7uHAgQNWG4lkMmntc4yFG2+8UcXDw8Mq/j7Y+6xcudLaDswXJB7X7xtCOp229htur1ZmaYLnwn7HuuDxaGGBdcJj8Zzl9hMaxZ5CrnF7u/48q4bpSDJrkc81knRsujK7WsY/Hu86V73LE8ncwNxtvNwlpATz181c5Azzl9TKdOwQZtImqh6Zi/cZswznPFEDvectFLjSmBBCCCGEEEIIIYQQQoiCk8aEEEIIIYQQQgghhBBCarOnEPsJ+cOF9Lg4PFfQ/4qATFu45ClXqfjQSW3HsHbDFhWvXNGj4n/6mrZJePjhPSo+c1rL2s0V/R1LtHz9Bc97vopf+qIXqfhIry67t/+MitetXqPbAS2MGV4TWR8sKWIRa0fk81r2nAOrgwJYLqAdxtKlPcF2dC+zyvZPD/SruLWlScetLbp+KS3PHjit2xeLBeXnzQkt6X/5S69X8dHjT+jyju1T8YqOFqsdAMr8zevR0tJs3Q/7oVpZRsSQmLssB3IgPccy0nA82lngWRPRYF9FoYpob9FISB+U62uXXYNw9dVXq/jYsWMq3rRpk4pXrVql4ptuuknFDzzwgIoHwIbFtApAK4frr9fj8eUvf7mKjx8/ruJTp06peN26dYF2utqBFhGmHYNtH+wT3I7jH600hKVLl1ptLPr7de42N+u8aGtrs+Y69pVZV7SxeNWrXqXiw4cPq/jkyZNWews8F1pVmGOjtbV1yn5wWXm4MMvA9wE878TEhPUYLA+tKhCzr/AYczw0Ch0dHTU9ubkWZvKJzo0kuZstaXGlT4GmXJYwd+sb5i4pB/O3vmH+EhfTve61WFK4oOXC2VRi5cZ+mxu40pgQQgghhBBCCCGEEEKIgpPGhBBCCCGEEEIIIYQQQhScNCaEEEIIIYQQQgghhBCiqMrkUSyLwba4SBg9J8EzcvdVVwb2a1u+QsXH92lv0iXd2hP0h9//dxUfPXpExWPjIyr2c9r3s+BrT0yhp0f7Iz/vOc9RcT6t/TJ/esvPVNza+aRXpBAB49rVPdqfNWd42MbE1LlUFx+8crPa99OHemH/xOPaxziV0567iaTeLkyOjal4cGhYxcmk9jFetXylitua9PFR8GMuQNmZgu4DIZvSZaxaqv1kX/vqP1Lxxz/0ThW3Nmkf1/a4jv0QekMZvj74b/Sbgf7xob4h8I9Gf5owbA8Zv3OgV3MUfE4L4JHqOaqBtY1FdZu84LDyQjA2wuBjXQg9+dcISH+anj/oC4vxVVdp/3Fh2TLtsf3444+ruLu7W8Xf/va3VXzkiM7d0dFRqzeuWZeeHu3r/cIXvlDFWfCmvvnmm8/yeTW9alevXm0tz2wjlo/evi7vpEQiYa0T+gsLIyP6vWpwcNC6H9axpaVlSl8s9PsVxuD9AT2V3/jGN6r43e9+t9VDuampyer3a14P9Bt2+b/hPngu13YT7FM8BvvXdV6Xj7HZDuxT9KJuRGrxTavEq226Xooz5bU8336ClXgbVlLHSttBj0XC3J07ZrKOzF0iMH8bE+YvKXd9p5MzlXrrztS95GLz8qW/8fzClcaEEEIIIYQQQgghhBBCFJw0JoQQQgghhBBCCCGEEFKbPUUolPNC4bAXCmmZcDyhZdcXX/00FZ97+VMCx/b2a6l2f/+Aivc9vlfFo6PaimEirW0oMhkde+AWYa5Af9512pJi1RotAf/lL25V8Z2/+o2KO1d0qTia0F3R0d2p4qZwsIvyIV2B8ZSW3kciev49GQO7CLS3gH6LxPR506nJQBmZjJZnN7e06vNGdRlxOD4ClhnHT2nrj4cefgiKDnbWVXCt8uCxcP62c1T84pe9WsU/+9H39MF5fT3iEZQHBIrwcrmCXUYA+wQsRvAEYfvmkOkdAYTBfsBldYGFR7BOsEvEkNSj7QVaboTCoYaRMEk95Q/l/mgPcM0116j48ssvDxzb19en4lOnTqn40UcfVfHwsM7dVEqPj3Q6XZEdwvXXX6/itWvXqvinP/2pim+77TarLQO2o6ury2pbYJY/MTFhta3AY3A7Hot2GGhtYf67tbXVao2AMZ73xIkTKr7//vudffX0pz/datmwY8cOFb/yla9U8b//+79b98f2mePYJftBKrGqKGdPgbjqgmVgv7vqhOcxjzFlTY2SuzMla50LmWolErGFKCOrpR2VSOsol114MHfrC+YuqQbmb33B/CX1avFWKZWMtYU4HmtpRyU5uhDf5+oFrjQmhBBCCCGEEEIIIYQQouCkMSGEEEIIIYQQQgghhJDa7Ck8WeVd8LxcTEu4d1z5VBWfc9nVKg7FmgKHZlKnVdzXp+0phgZ1nM1qKXsur+XOhUxOxWGwkbj80qAFxjOf9gwVj06Mq3hgRFtjdHQv0efy9Zx530ktuz+xVMvEVy3TMnjBD0GXhaGOhbyKY1EtP0+D1UQmq+OmZLOKIxG9f/HfYIGRBzuGttY2vU9UL7+/735tufEf//YtXT2w+Hh83+FAGYdP9qv4qqvhGm7YoOKX/f4rVHzeueer+I5bf6jiM0cOqDhl2Gxg2/NwbcHJwwNnDS8Kv2GEIiBFDxhaBKUGYbD/8HE/sAIJg/cEngm3o1VFzJC4h7FIHJeFgleANtYzJfsAtAu48sorVXzZZZc5bR3QbgLtKc6cOWO1ZcjlctYYbRme8pRg7j772c9W8djYmLUMtJ5A0NZh2bJlKl65cmVFFgiuOmK7s5C7yWTSaYfgsmNoa2uzWibcddddKv72t79tLe+xxx4LnOvYsWNWq4qtW7da7Sl27txptfs4evSota2mrQj2D7YP+7Cc1YUL134umZFrf5d1iAm2QyRL2MeNTKWy2JmStE1XDjrXTPcJ9TN53ulI6xqt38nUMHfnB+YumQmYv/MD85dMl0otKWbK3mC6VgwLhelahFRrJ8N+n1m40pgQQgghhBBCCCGEEEKIgpPGhBBCCCGEEEIIIYQQQmqzpxiaCHuRcNjbfP5mte3cy69ScTyuLSkKeS0FFlauWqriDRvXqPhUU0LFA/3aqiKT1XL35i5t5bDjgh0qfuUNNwTKWAby9YFhLWtf0bNCxU+9RlsxHDmkLRsKeW0v0dKk2xEOGBp4XjgCy94dsug0yJ4nwSIC7SmyBb38vb1FS9fN5fQtzVoK39qi+2HvvkdVfNNXb1Lx0IEnVJzwdZvGQ7qfha99/ev6tYy2lWh/4fNVvHqFtqrYukVL39eu/UMVjw3pazYxrm0FiucFm4Hjx46ouPe4lsX3n9BS+/FhbSNSyEzoE0F/RMH6QwiD10UErCsiYB2CSgPs2yhcP7zOYbC2ePJcIL2Hn1n8XD5gV1HPTE5OFtu+fft2te3yyy9XcSKROMvKosSqVatUvHmzzv0myBO0rUDZf0tLi4ovvPBCFb/2ta8NlLF06VKrJQVaTFx77bUqPnjwoLW+WJ4pS8EcDYwD2I42G9JntjahzQHaTpjnbW7W+dra2qrixx9/XMWf//znVXz48OEp7TOEL33pSyqemNB5cgO8H65evVrF55xzjorXrVun4qGhIasliDA6OqriI0eOWK0xjh8/bj0XWlsgpu0JWoEg5viz2VC4bDIqvebSp3l4z28EpiuRdMm3qn1yc7ky6kXuVUlbTartE9extew3k9I613mrrROp/9ydbtn1mLtzDXOXTAXzt35h/pJyTNeeYDpWFZXapdRy/EKg0v6sxI5munYyrvNWW6eFDlcaE0IIIYQQQgghhBBCCFFw0pgQQgghhBBCCCGEEEJIbfYU+WST54cj3u5Lr1Dbepb1qPjQgQMqTqW0tFsYHBzW//D1XHVbW4c+ZlJLw1E6vHrTWhVffZW2w9i0Vm8XcnmQxYPcftVyLX1fvkxbVSzp1GUfOaItE/y8e5k7SqcTcS2vjoBNQjqjLSly0I4JkLujADuZCMq0QyFdZgasIwaHtEy9GWTxr339m2Gf0yru79WWAbl8cLn96jXrVZwHG4tbb/+1ijev61Pxrp279LkKur4333Ef1FX3v3DpxRer+KJrzlNxa1IPuwmwtxga6FfxSbCtOLhPy/n7TujrJKTBEqNQ0OWHQV4QiWh7iWhY1z0EdhYhuCKFQlC2XoDr4UE/5v2Cl/Mbw55C+kAk/VdcAbnbo3N33759Kk6l9PgVTp8+bc2Hjo4Oq5UD5u7GjRtV/IxnPMO63bRgQNsLtKfA+nZ2dqr40KFD1vw0cxfrlUwmreMD2477ow0EnhfPY8pa0OpicHDQamnx5je/2WrL0dvba62HaTGBr/3iF79Q8aZNm1S8e/dua//ccsst1rqa1iV43bC9w8P6PX1gYMBqYYFWHGhnYfYp1gutJ/DauCwpELOvXPtJeS4bjHpD+rm9vb0m2dp0JHC1PKG82vPOlIR3JnC1d7r2HdN5InS1fViuDBd8Mnzj5m61xy7U3HXB3K3tvORJmL/1D/OX2MDvp9X293T6eLqfM9VaMUzHPqPRrUOq7YdK+2q2rEP8Brs+NrjSmBBCCCGEEEIIIYQQQoiCk8aEEEIIIYQQQgghhBBCarOnuOzpT/Xi8bjXAvLqn/zkx1b58XGQKAtjY+MqLuRgibavl3g3Nbeo+Jxztqp418XaGmHF8uUqDoF0uXiqAixbB5ly38nDKo7GtdSos2OJike6xqwS7HgsWEYupy0Q4l4cXoGl6tC8AsjuowGbBL3PZEr3TfFwOBXK1NEmIAbWGKtW6j655JxzdBmwfD5aCDuX1o+BlciXv/wVFf/yF7ep+E/+5E0q3rRZl3H+eTtVPDKm+1C4/+GHVfyTW36q4p07dqj4qssuU/GWFdqy4Nyd16jYB9uJbDbYV6dBFv/onodUfGjvYyoe7NWWFpND/dZri6KBCF7LYl/pseR7+phIOByQzNcz1157rReLxQKSnR/+8IcqPnr0qDUWRkZGVIySfoxbW1tVfO6556r4Mri+K1Zoaxiz3/BcaDGAVgdoW9HV1aXiMRh3uE80Gi1rXTAVaJnhskxAWw5TfnLixAmrfYO8h5ZYvXq1td/KjSvMXSz/s5/9rIp//GP9vvyOd7xDxVu36vfVCy+8UMWjo6OBMu67T9vO3HzzzSreuVPn+1VgFYQ2IhdddJGKn//85zstMPr7dS4++OCDKn7iiSdUfOqUttgZGhpSsYzlSiRDLjlQuIFyt5SztTwdeKae1D6TksjZkldOV2JbrUx1tp6uPh25bC11nC2ZHWW0zN35hrlbXZnTOe9CZLHkb6Nd73rN32rrxfydfWsZlxXJTNoIzKT10XTs0xqB6dZ3OtY75c5Tbb38WbKWqffr2RjfmgkhhBBCCCGEEEIIIYTMCZw0JoQQQgghhBBCCCGEEFKbPcXpvjNFWfC/PvodtS3na8n36LCWseeNJ9OHYVl2LKbl2R3wZNqdF2h59s4LtSXFhk0bVLxqpZa4myu9o2A3cPTgERXf8ZMfqfjcC7R0umeTtllYtqxbxa1t2ibD94PtiIIthA/y5kmQXqO9RCyRgO36hXhU90EiiTYXYvOg+3HZimUqXrFKS8DHR/U+w0ODKk5Gdf2WL9PHprFSxXbp69bc3Kzi17z6RhV/67v6Oh/v0zYZ7Uu0NcDGtbpOTXAeYed521V88vhJXReQ1GfBMSAc1X2VAhsQtP7wI9p+QGhbvlbFW9B6ZK2W4R8/vFfFjz90vz5rNqXjjK5TYTxos5GD/RJxlLpEvXyDyIBE5i92DXv27LFaQgwODlq3m5IKtFbo7OxU8a5dOl8vvvhiFW/evNlqxWDKNNBuYP/+/VabBSxj06ZNKl4OljUtLTp3TbDuWH46nbbun4Dcxf3R9gLtMEwrj56eHmvb0QoC+x37AK08yslXMHff8IY3qPgb3/iG1SZjyRJtybNhwwZnv6ENBVqEoB0G2n1gn2SzmLsa0w4CrxuOmbVrdU4fPHhQxQ888IC1bCzPtAvB1/D6i8UI2ow0msxuJplJWWy10smZlGLNlBzYPNdsyUFdZUy3vErONd2xVIlEcLp2IQsB5u7ChLm7OFjI+buYmYv8reX4SmD+1m4tM5PMpCXFQrAtmCtcY3u6fTgX18Cv4P2+3u+xuNKYEEIIIYQQQgghhBBCiIKTxoQQQgghhBBCCCGEEEJqs6fY+9hjXjgcCVg0eBE97xyCldRRsBQovgbz090gkd64UUukL75Ey88vvfgyFbe0t6k4HIbzgsWCkMtrmflAv5Zk58FuYKRfS62Xgt3DitVa7h6L6G7x81mnrUMmBVJoXEYOy8uzYFuB8RKw5Ug2JQNlZLJ6v/7Tp/UxYAuxatUqFS9fulTFhZy2FphM53T1wjo225jL6mO6u7Qs/i1//KcqHgE7jLHxcRUPjQ7r7ZN6u/mLRPcSfQ3jPbq+IVCIP/DIQyret/+Aip9+zTUqbgILElN+Hi7o65/N6z4cmNT7ZNp0+x7/7WMqjsLYWdLcGiijq1Vfq8nhPhW3xfNeziHFrzfElkLsAUyJfiUyDfx3d7e2cdmyZYuKL7/8chVfccUVKhZpn8ueAMnl9Pjs6+uz2g3g9pUrde6uWbNGxdg+02YDpR6mjYGNDOQrWligLYdpT4H7DQwMqLirq8taX7RowD7A+pnXCa0g8JhlYEfz9re/3WqZMTY2FpBelhiHnDavOdY9mUxar+f992vblyeeeELFz3rWs6zXxsxdtJvANmF90YrjkUcesV7ntjb9PmNen4mJiYD1CF7fRpDZzSTz+aT2epFOVvr05Lmo41z0u4uZlOTOhcSvkeS2zN2FD3O3chptzDB/Fz6NnL/V7ldp/i6EsVGylqlXS4rpvD/Uo23BfDAX/T4X+ebP4PvAbI8NrjQmhBBCCCGEEEIIIYQQouCkMSGEEEIIIYQQQgghhBAFJ40JIYQQQgghhBBCCCGE1OZpPDo0XPSxbAVPz0hM+xuj5YZfCPpqxOLal7OnR/vKXnvttSq+aLf2NC6AL0cOvUnBf7c1Cd7KnuelwFM3ldbxxi3nqHgirX08T/dqf+OWNu3V2tGdtNZDyGS132YYfIFx9n0C/EhTqZSKk4mEipvAGzQH53wS3ZER8A3N5bQHZz6ny46Cz28e9k+DZ6hfCPo/I/EoeCqHdZ9mMtqf1YPr2dKsfUJzBfBNNj1XfH2tUuD/nAL/4NYW7UHaO6A9a2/91W9UPJnRZWzbpr2nhSaoC3qkgrWz17Ncj7fJMX09cindn088sV/F+VDQC3dJh67jqoTuxx0rkoHxUO/+TuKJg3686I1bzi8JvWjRS/u6665T8SWXXGI9Hv1m0avW9AJGv1n08926das1l3p7e60+tujri+WZ/3b5OWMZWA/08sXYLANBz1/cD+MYvH/i/uiNXK4M1/Ho14vXo6WlxeojXA7sB8wx7PdTp06p+Je//KW1Heeee27gvDgG8Lw4Znp6eqz+xnid0EPZHLs43rG+a9euDZS5UJlPn15XPSrxZpvJ+tWT/1u9e0rOlmffdNtdT9dwrmDukoWQu4uVeswPV/7yujZ+/tLHurG8i2f7c5jMHdN9r28kH2uuNCaEEEIIIYQQQgghhBCi4KQxIYQQQgghhBBCCCGEkNrsKTyR7Yc8r5DXcukQSKIjYNdgrsrevn2bil/4wheouKmpWcXf/Oa3VZzJa+nw1U+9RsUb1q6D8pKGjFpbUvg5LV9evXGziscz49a6Hzp6VMXRvgEVL1uq5e5Ca3srlB+2WlKMTWqpfQLk40uWLNFlh/SxhVzeOZOfiCescnKMx8Z1m7DjQyDBNyXuIah7uBnq4unzTmZ1H46nxq2WGWE4z+jYaKCMAwcPqXjLlq1WmfjEpJbRn3vOdr1Pc7uKjxzVNiJHT5wMlIEWIQNgb7F+zVoVN8MYa042W6+fH9Lt3rJxY6CMaFLbM3SEtdw+Ekl5kcoU/vOOyBzkD8cN2hmgXYMpqTjvvPNU/NKXvtRqL/BP//RPKkbZ/9Of/nQVb9iwwToGTHsKHKubN2+22iRg3Q8dOmS1SUCrCqG9vd3aRrQ6GIdcQvuOrq4u67HlrCPQxsKVu6Ojo9bz4vUwy0D7BrSbQPkK2kJgm1zXHOsh7Nu3T8Xbt2+39iFesx07dqi4tbXVem2OwnusWX5fH+Tu+vUqbm5utsZ4bRAcL+Y1QJsVKRv7caFSiTxqJqVVrmOq3T5d5tr2Yj5ln9OVr7rOhfvPRfvMdix2Ke1izd3FxELJ3UZ4n1ys+etiMV2L2WIx5O9ipJLrOpN2ItVaD9SLVUEjM13riEqu5UK8TlxpTAghhBBCCCGEEEIIIUTBSWNCCCGEEEIIIYQQQgghtdlTFGSpte97mbS2FIiBzUIkGrPaKgiDZwZV/M9f01L24yd7VTwKcuedu3aq+JqnaYl7JKarHAO5sTAMMvOThw+q+LzdV6q4bcUKFfee0jYUJ3sHrDYL2zbrtgprElo6HYYl6dmMluQnE7rt3Z3akqIJtvsgUTeXyQek1/BaGvq94Ou+ioEFhudryXUW9s8aEvfWDpB6o02Dp5fTT0J/nho4reLmpLYlaAEpet/poUAZd9/3oIqHR/W5dl+0W8URGD+JhI43b9UWEavXrVZxrhD0g/Dzur2PPabrvqyrU8WxuK7viWPa6uKJPY+qeEmntkvo6+8PlDE5ckbFG3Zpi4WmZMQLhRtD4i5SfBlnaFvgskNIwDgV+qE/brrpJhUfg74cGxtT8cUXX6zia6+91nrewJg1LCIOHz6s4ksuucRqN3Hy5ElrjPXYtk1b4phlotVBJpOx1rG7u9tqc4C2BmbuogUC2jdg+1CygnVy2Utg/cx6uY5HKw+07MB2o0UIXmPhrrvuUvHw8LCKL7/8cqvVBbb7nHPOsVpNoC2H2Y979uxR8dKlS63nPXLkiIofeeQRq+2P2Y6RkREVX3bZZQFrFRzzi4G5fkJvtdLZcmXPhdS3kifLz4XUejrSyEqlctOVv86G/QdluIs3dxcKizV3SX3lb7Uwf2fm873a67mY+rZRqcSGYj6vY7myF6I9govpvpfO5D3PQoYrjQkhhBBCCCGEEEIIIYQoOGlMCCGEEEIIIYQQQgghpDZ7CrECKBR8z0uirB3sKUCunM8Hl2739WnZcB6sEnKwXzyuq7N121YVL+nUVgOFnLaBMMkXwC4ip6XPYU/HrSDPPnDkuIof36cl8b6v67d106ZAGZGwrmM6resSA0sJkT6rOKFl7WGwYsh7OWsfFuvua9n0REqXcWZo2GqB0QHWCjmQskdghX0C5PVCS0uL/kcYlvXDPmeGtKXIiV5tAdCzfKXePwyWJE26HsJV12hbkYlxLdU/PajP2wbXIxzB0rWUPZ3SUvusIXFPRGDMbNRS+ChIz3M5fd7B06et42JwEPq2KWh7cvWFW1S8slu/FikUAvYa9UzJCsBlSYGWKGgbYEr+s9ms1W4AbR22b99utVLIGRYptvqZZbjG7MGD2n7m8ccft55n61b9HmK2ES0f0OIBcxctKdCKAdttWhxg+WgRMQhjHvuqE97bsN14nbBOZj+4JDlnzpyx2nf09PRY24S2FcIzn/lMFY+Pj6t4YEDb+LSCNQ2eC604MDavK1pPbNmyxXqdcMxg2chpyGmzr9AqBe1NFqO8qF6kkJVIrad7bWayrdOpy0y2o1JZ+VzIz2djLC22fKyGxZS702U+rWUQ5i6Zr/ytNgfqKX8biUqtZuqdSq95I7VpppjrfKjWJq1ebDIW0nVtpPfAUIXXfKba1BgzXoQQQgghhBBCCCGEEELmBE4aE0IIIYQQQgghhBBCCKnNnkKWQYvFQt7XEuwQyJI9XDIfCkrcC3n9b1S/h8Na3r16zRoVX3nllVCuPu/ExJiK80u6AmW0tLTrMmA+PJPVUvRsWsulz9txjorvvOcBFS/p0pLxWDRoVTA8PKLiro52q11EK0iko9C+wPLwkN6eLwQtF04PDql4aGhUxePjWu5egGsQHdeXMQaeFM1NWnLe1RHsq3hcS+8LUK9JsILoB9k37o/1HR0dscr2haa4lv23t2ibglhUb4/FQSIV1vXIgZS9AOeNg5WAEPH18dGYvlYhsBgJR3Vfr+hZoeIukKu3w7W8YJu23xCWxXQbo2B1EY5EPT+tx1a9I/mL9gkoa8DtaDVgXlfXfmvXrlXx0572NGv5aHOAthWm1QGClgbpdFrF559/vop/85vfqLirq8tqcyAMDem8WrJkidUuAq0fXJYdrn4zbSHQkmJsbMx6DFpjYHlYD6yrWV98T0E7DLQUwTLwWmJ/mO3AMtra2qx1RHsJ7BO8Zmgvgec0cbUJ675q1Sqr1URHR4eKd+zYETgv2lXgecVWBMcTmR8qkcvOR12Q6cgCa5GOTbftjSRRLCeZa6R2LEYaIXfnuuzpvg800phn7jY29Zq/9WLlsdBh/jYuldyTznddFnrZ80m5/JztfuBKY0IIIYQQQgghhBBCCCEKThoTQgghhBBCCCGEEEIIqc2eIvy7P1z8nM9puXPeA3uCSHCJdD6nJcthWFodiegqnLNtm4px9XUkque2C5mcc4l2DOwJonEtd9576KCKL1mqZfErurUlwaWX7lLx4KC2hAiFgnYIIyP6tTjUq7O9R5cd0XYI2FvYI+msbseJU6cCZfQNaFuI0TEtOffBFmLS13EUbAKWdGqpdmubbl8sUCfPS02A1QV0Y29/n4qzUMck9Ccem0nrOJYI9lUcbCjam5fq+oJtBcri82Bh0tak5flxtJ2IBdvhwTG5VNpqaYH9vnPXhda2blu/XJc3obcXjx8LuWUADSKJkFwx8wWtCvC1cvYUuJ9I/G22ALgP2hmgbcHZuRuzxnv37rVaWqxYoW1GrrjiCqslBNZPGB4ettYL7Q3MY2xgO06cOBF4ra9Pj53R0VGrZARtOrA8tKFob2932mxMTExY69Xb22utI1o04LFoz4BWE+a/0TrEZXWBMe6P5zHbgdYVWF/T5qbExRdfbG3rpk2brG0yLTsQuR6LSc40XXlmLRLOSqTatVyDmZLV1iLPrFYWOJMyz0r7rZKnb9cL9VinRmK2ru9c5+50zjNbdefYLA/7Z/Hl70wxW2XX++ddPcH+mR6zlUuuc9XL2J6tsuvlvakR8Oexf7jSmBBCCCGEEEIIIYQQQoiCk8aEEEIIIYQQQgghhBBCarOn8H/3F5BwB5aUF5xL2IOyb/1aW5uWMmfAXiDu6f3zOX3eRELbJESMMnKe3q+zu0sf09qs4jGQhmcKWrq8deMGFXfs1lYFI+NBefPEqJa4xxNaep2DOuZCYLkA0/JnRoZUPDQyouLTIKkXek/1qzgE8/pNTbodobC+dEMgu+/vH1DxkSPHVNzSHJSf58B6Ip4A+TrIuXNg/YA2JNmJMRVn0ro/Y/HgbxArlmoLgaXdnfoYkKUnwPYik9IS9WaQ1KNcPZVOBcrAc/lhu2wikwGrCljVf9nuC1TcGday/YOPPBY4vikSt1qE5DJZLwfnbgRc9gvlpC+uY9ra2qw2AGhvgRYEmLumBQaWuXSptjJpaWmx2jrgebdu3arirq4uqz2EMAI5h3XBc2FbcdwNDUHuQnzmzJlAGSdPnvRsNDc3W9uKdhqnwKbm0KFD1j4QMpmMipPJpLV/0PYF2+eyp0DbCaGnR9vtLFu2bMqyU6mU1Q4D7SlM6wiX7QmCx+AYveyyy6xlPPTQQ4Hj8TU8Xs5r1ofMjSRtrmV2lUq5qq1LJfvPZNmufjPLoLSPzBbM3emXPddlEFIPY6oWS5m5kLgv9Nxa6O1bjMz1PV65MTRTdanl3mKh3+v6ddg+rjQmhBBCCCGEEEIIIYQQouCkMSGEEEIIIYQQQgghhJBa7Sme/B8uHI+DrBlnoAsF3ykTzoPtQSajJcK4Iv3okSMqbmrT0u5WkD4HvAbk+IhuziTYBjQV9IlXrVipyzilrRyOHzuoy+jU8vj+AW0VIbS16Lq0gzw/l9cS8DFfS7VHR7WVw9i4jicm9T4jI0EZ/fjYmLXfYlHdw9GIltd3LdHWD03NTVYpuimbP3L4qIrTKS05zxd0Ozq7lui2tmqJfDivbSTyaV1Gc5Ouk9C9Xcvas3DNcQT1gZ1GMq7rPjKmzxuBgRU2fueIR/T4S+e0RcJ4Vpc3MqHbN9Dfq+KV7frYx+992G17AlL/fFq3PZ1Je6mM/nejyRxwbJXbF60c0FIgm81a5SRorYAWFmjRYJaBdhVogYDWDCtXrrSO58OHD6u4s1PnQl9fX6AMrEt7e7vVvgHbh/YWY5CTmFfDYA1jHoP9G41GrRYY3d3d1v5Bq4njx48HysD+RVsQtKRAmw5sN/Y79jNaSpgWIdg/CNppoFUF2oBgu02wH7AuaHWB/d7b22tt3z333OOUNWGfoB2FlIdlLmZcks56kjVOp16z1Q6XdAzLM8uu1GKiknNVW8ZcM522kspg7jZG7rpg7i5uGiF/Z4q5btNc5G+9stDHUj1Q7XhqZOa6TWZ51b5PNvI1CNX5WOJKY0IIIYQQQgghhBBCCCEKThoTQgghhBBCCCGEEEIIUXDSmBBCCCGEEEIIIYQQQkhtnsae+I6Gwl40FLZ6gBbAiwP9Kk1vyXAY9gPPy71796m4tbVVxes3b1BxDHyLTbePAvii9qxZreIj+55QcQd4mW48Z7uKz4xrn9Kjx7Tf78io9ucUtm3drMsH3+bREe1teho8inM53W4P4ol0yun7uWbNKhXH4LVMSntyNoN38Ypl2hc1ntT+sxPN2md0HDyChXBYe8IePaY9Us+c0R7Ora3aY/Xiiy5ScUtSX9tx8HddsWJFoIx4s76Gx47r8lrg2rbB9UBP44Kft/ohJ+LBvsqCp3Aa9hsEL+nDB/W46o5pf9YTj+1XcSiv+zabD3qdFnw9rnIN6oMqnsHilYN5ibkbqjh3w9b9HnvsMauH7pYtW1QcA/9zEyx/zZo1Kt67d6+KOzo6VHzuuedaPXSPgBc6bhe2b99u9QpCX+KhoaEpvY7RR9jM3bVr11rbi3666NOMOYPe0ehvjL6+5jXA9p4+fdp6DS655JIpz9vT0xMoA/dDz2j0gsYYPY2x39D3Gttn9ikeg9dj//79Vt9lHG94HozN89LDeHaoxG+s2u2VUq3n13T9/SrxP6zFh2w69Srn/+bab669Yl3Qb3F+Ye42Ru7OBczdxmMuPJFnyrt1rsdLpfVbiPnL3Kx/5sITeabeH+baW7fS+k2nXuU8z+eaUJ37GCNcaUwIIYQQQgghhBBCCCFEwUljQgghhBBCCCGEEEIIIbXZU4RCYS8UDnvyP03YKscOG0u/Q2hJAbL2aFQfk8npZdnjGZBzt2updTwRt5T8uzIgbu/Ulg2rN25U8cm+XhX7MX2upUv1/qeHteRb2oscOqStHEaHtZT91El93tExLYvHlebxmJZnR+K67NY2bdcgdHV3W2XmnV1LVLwE4mgMLDugwCTIwdev1XYdxXZB7508oes+NqLb1LN0mYqzk+MqTvu6vOUrV6p47WZtRSDkfX1FWtu0tQAai+TB+mAQ+hP3yYC0Hy1BhFxey9+jUd2n+VEtcW/LaRn+6ZP62uYm9PZwTI/JdMaQB/hgU4DjWMZ4pDF+dxH5Q+nPRhzGo7mPy5IC8x1tAFKplNXCwLQncLFkiR7bGzF3T5602kIsW6bH6ZkzZ6z1Fg4cOGC1QDh+/LjT0sLWVmwH5qewdOlS62tdXV0q7ob8xvMGchfsHtatW+e5OHbsmLXuy5cvt14PZNWqVVYbEbMuaAvisi3BfkfQlsOU3eCYwX4YHx+3jkW04sA24bGmBYVrvMvYMMcHmXtqkc/NtcwOy6ikvErrPh3Z/3TbMV0J3HT6gdLZhQFzd+5yd65h7pJ6y9/pHruY8pcQk7m2lplOnpSzfanWOst1HlIb/NZMCCGEEEIIIYQQQgghRMFJY0IIIYQQQgghhBBCCCE12lP87n+BbbAKvADWAT6+UJTyh62WDytXr1HxxKSWMq9dqyXZzU1N9voY8uIC2CH4HtgNgO1FKNYM5Wlrg9REv4rzsLT9xDEtXRcO7dcS9yiUnwCJdDSkpf4TE1pqPTo8quJsPq/rCpYHQhLa29GppforVvSouL1DS99X9mh5/pIObQeQBCuPpmTQGmDDBt3vL3rR81Q80Kf7IZuaVPH4mJbzN7fpPly/Scva481BqX4O2oiqAJQODA8O6vJyev+x8QkV5/NaBu/ngvLz7iW6zPzkaRUPHXtMxYMntaVIKKzPlfV0ebmMHgthsGAoAjL8AlhVePmCl4c21jPlrCkEbIe5n0sasnbtWhVPTOjrtWHDBhU3N+uxgpjWAC45CdoNoA3B5KQem6Ojo1bLBLQzEJ544gmrvQVac6D9BtokDA0NVdRXTZC7aLOxEmxc0O4BLSI6Ozut5zH7cPPmzSp+2ctepuJTp05Z7RvQtgLtQtCSwizD1Ua8TmhJkc1mrf2GFhQYmzYdWF+03Ojt7bXWA+uH58XrZ44Hs32NkrszxXxKtqZbxkzVsZanMlcis6v0vK5ccpUxk2VP9xgXlOvOPsxd5u5swNxdOMzWWGL+Tr/s2YL5uzCYres4U+et9DzV2kFVel6XDcV0bJTmO3dCDWqVwZXGhBBCCCGEEEIIIYQQQhScNCaEEEIIIYQQQgghhBBSqz2FX/zL+yBdzmtZMjhQBH0rZHY6pKXlq9dqa4SOLi3VHjt6QsXnbt2q4uZ40mpzYcqdQwELDN20QkFvL/g6Hh0b0/VLtqg4m9I2GclocAl5V0eXiicm9H6HD2kpfGpSS/VzeS2vb0pqGfz6jVrC70Fdhf7T2rLhdP+Ais/0azl4GCwt2ttbVbxytZbBn79zp96+bHmgjKaIvoYbwU5g7Wotlx8aHLIu5W9u1dL5MNQ9HBgAnheD9qJMPAc2FDhocuP6ekTABgLjqK9tCYQ+sBw4c0rL2r28PiYFNhtoPYEj1AcLjHAoKHFHu5I8SuFDEcOspf5xyfrLST7QSmL9+vVW+4UxyKXt27dbbRbwvGfnbshpXWEDLRcSCW29kk6nrbYTZn3RTuPAgQPW7dhXyWTSag9h1rWvr88a9/f3W49Bq4o1a/T74kUXXaTiFStWBMpAC4ZNmzZZ7UIGwfYFc7elpcV6HrMdaAWC/egaM+b1tO1jlrF3716rDQW+V6ANiWtc4HUqZ3uCFhpoT7KQadSnCJd7erJrv+k+7bxaaV0lZZQ7T71fg+n24UyW2Uh9NVMwd5m7jZq7pHHzdz7GIfO3cjiuZp/pfh4tNMqNrWrtZCo5b7nzNPI1CC2AccWVxoQQQgghhBBCCCGEEEIUnDQmhBBCCCGEEEIIIYQQoqhKoxsNR4ry3xBIgEMRHcejWu6cM55Mv3xlj4q3bN2m4v2HDqq4qUXLzHtWakl2HKTS+YyWGBeieruAK7xzOVwyr+sYS+oykgl9fFOblq5HYrpbutqCEveJiXEV33fkkIonJ0f1eZO6H5a2dKp45QrdB6tAip4DawRh9bJuFfceOazLHtL2FB4ck+rT0veDA3r/0V5dv42btd2HsGatlrW3deryCmDaEIb+mcxqm418OqXiGFpS5INL7DvbmnUdwb4gCbYVYU/L2sOePm/fiX0qnhjSFh2pMS27F3JZqAuMkwJYYKShvtGYvv7hcMRqYZIrBKX22YxuewTUBTL2MRfqGbEiEGmEy5IALQhQxi+sXKktT7Ztg9zdv1/Fzc36Wq9atcp6TfC8pnUEyjPQ6gC34zFoe9HW1ma1HUDrB2F8XOfuwYMHrZYUaHWB58U+WLdunbWuppXEkSPasmZ4eNjapoGBAWuMdg3Y56ZFSGenfn9BsN8zMH5ddg9oCWH2HVqPYL/j8djvJ0+eVPHQ0JD1PGa98Nqi3QTajWAZOI5dFhTm8VhfiSuxQVmoEtD5pJInIZvtqFaSOV0JZ7XWCNU+mX0mMetXbd3n8ynxjTBe54pG7gvm7uLLXVI5jXwN5jp/q2Wx5W8jj6VGpJGsA2YrR2erDyr5LjAfNloz9bnqV3gv1EgsvG/NhBBCCCGEEEIIIYQQQmqGk8aEEEIIIYQQQgghhBBCarOn8H/3F0FZMlg5oCFF9/JlgWPP27lTxf2ntc3CiWPHVPzcZ1+r4qVd2jIhDjYUOT9rlSgLE2mQYae0JUErSK3jcV13H2qcRpuDiF5CHksG59XDed3e7Zs36P1gpXoymdTt6F6q4pbmFhWPDY3oNoHlhdB/XFtMDPUfVXE+q6XlEeiTZpCMt7S06jadPKDiPad0PwsPeLeruG9Yy/PH0tC/US0Zj4M0PJHQjU1Cw9ev1rYEwoXnb1fxE799RMWxkJbChwpZq/x8cFDL2iNewbpP8fgwWFLk9bVKw/VHolDfQg4sPlJpp1QfVQgRlDAUCsW/RqAki0BJfsDOA9qBFgvCRRddpOLTp09b7Rde8IIXqHjZsmVT2lOYuYu2CRijTQJaGKDMw2VhgHlotnH79u1THoPtQPsNtFzAugpHjx612k1g27E8PG9Li35/6OvrU3F/v7afEW6/XefumTP6vTQFYx7LcLUP+xMtN4Sd8H69Z8+eKSU5WAbWCfc/K3fhNbw2aBeC4FhCWxC8Bmfnrr2+sp+572KVtVcrqSxHtXLwWvZxtXe2JHfVnneu5aNzIbOrpS71Il+uZxotd6d7nrnO3WpZzLlLGjt/Z4tq6zVTtgyzVfZMwvxtXGr5nHIdX6+2A9XWaybH7GyU3WjWMgsRrjQmhBBCCCGEEEIIIYQQouCkMSGEEEIIIYQQQgghhJDa7CnAVEBFPnhSLF2tZe1XP+2pgWN7T51S8eFDh1TcDnYKu3ftUnFbq94ewmXjYIdRMJaQnwI5+KOPPq7ifEgf39amrRxSk9oWIhHTcvlO2Cc9oqXowujwmIr7BrTFRBRsEpYs0ZYU0WRCxdmQlqinx7Xk/Lf33hsow09puTXaCURAnh3ydT9kUlqqHSroNoUCVgR6n+K/w7rvVrfq/hkNaYl7KKTtPjo62lXc3qr7BxXnTf5woIzMGW2JEcmOqngc5ORhuIbxhO6rZFKfOBzS18YvBCUB2axu12jA5gMsRsDKIxbWkvzBMX1tfRjT2OfFf8O5PF9nge+HGlqigPL8Vau0tci112qbGOHEiRMq3r9/v4rb2/WYuOSSS1Tc1tZWdV3QguGRR7SVCdIK7wloSZCAcYN1Gh3VY04YHh622j/g9e7u7raeF6/z2Jh+D7jXyF20ynBZgSBoKYHXA+07zGPxvJ2dndZ6obwG98Frg3YR5phHC458Pm+1jsAy0PaiCexyXBYUpmUHlofgNcA+wWuJ18ZsR7knazdy7taTxH++ZWXV7DOTcuJ6ugaVMFs2FK4yKpH71WtfzSb1NG4WU+42Mgu9fY3EQszf6dZ9OrZEM1H+fFGu3o3apoXMXNsd1VKXaplu3Sux2ZiulUc9Uq7ejdqmuYArjQkhhBBCCCGEEEIIIYQoOGlMCCGEEEIIIYQQQgghpDZ7ikgyUZT/xpu1FHn3JVeo+OLdF6n4wIG9gWNPg/x8dFRLkXdfpI85d8cOFafTWrbtg6y5rbVFxVk/aLlw2+23qfhH//Gfut5xLe9e1qOtI5riuvkrl2tZ+kRz3GpbIfQPaQuE06NaIt++pEPFPSuXq7gDrByO7NWWGXf+/BcqDoH8W4hDfcOwTD6VzthtHeJxq11DNAq2Dn5Q4t4MEvJYXP92sKRVy9fDEb09CvtEoU8KeV2/XC54PVAuj9JylKn7ICdHGTzKIUbBRiST0pL2J9ubsdYrBtL7gLwfVBYolo9G9P6+ocTw87pdOfBjiYWDY6OekTEifYrWAZdddpmKr7hC5/HevcHcRSsHtAW49NJLVbxz506r5QLKPNAawbQq+NnPfqbif/u3f7Neu56eHqsdwsqVK60WFqatw+nTp63tQPsGPBdaXWCf3HLLLdb2mWXimEfbCpf9Ato1oHUEXjPz39gPHR0dU9pbYIzXoJbcRSsIPB5zF21EcFyUa6+rvi4ZFR5rgvXFuNwxjUYtMrfZkl9VIv+bLZuEaqWHtTwFupIyFooMt5Z2zNZ1XqgwdysvG1koY4vy9saG+Vt52QjHNplvasnD2Rq31dp9TZfpWFLQumFxw5XGhBBCCCGEEEIIIYQQQhScNCaEEEIIIYQQQgghhBCi4KQxIYQQQgghhBBCCCGEEEVVxo6hgu+FPN8LgR1pPq19KR+49z4V9/WdDBzbB57GPStWqPj1r/0DFS9fpn2FhwfPqLipqVnFkbD27+3t6w2U8Ytfak/jgdPaN7m1Xfsgt4O3aFtzk9VHGH1RfS/o39LWpbts3ZZtKt6wfrWKm8Pa3/Oe229X8R0/0z7GefAARW9QIYW+vb7u7Ajs19SUsHp15tFzNK89aXI5wws4D96mWd3GbE57BLe06H4P5cC/NK/PFUEvYMOndmhwUMVx8G4N+D+Cj3EBPJRzOb19dGQEygj6CEfgGM8H32WoV9BjNWX1as2Bb3E+H2wHXp4InCsUj3qhBvH3KfU59j360N51110q7u0N5tWpU6esvsJvfvObVbx8ufbxPnMGc7fJOs7xnMJPfvITFQ8MDFh9kNEXGHMUPb0DuWtcm+5u/f6ybRvk7oYNVg/dW2+91Vo/9OI1fZ/QlxvBtjc3N9tzF47F/bE8s454PO6H/YB1xGuO49/sK7yG6LuM+2F98VxYD/SONsvAYxD0G3b5IwdyF9pkejPjfnguKcP01W5UKvUsnCl/wel6wVVyvFmnmfJdq8WTrpJjpuv/Nlu+k65+n2sPyun64C1Ur2TmbuV1XEjXnSwMmL+V15H5S+qJSvNiprx9p+t/XsnxZp1mKkfpY0xKcKUxIYQQQgghhBBCCCGEEAUnjQkhhBBCCCGEEEIIIYTUZk/R2tZalPtmQZb8wP33qzie0LLpkBeU/nZ3aWn4H7/pTSpev26tikdHtJS5tVVbSoyPa1k6uAh4t/1Sy8eFg/v3qXjLJi05336ulqKvXb9Gxat6tKS+s13L4H2wX/DCQQl1a3uHijtAZn761DEV/+i7/6riPQ89aJ2j930tA0iltCWEEIno12IgvY+ALD0PaoFcFvoHrk0eZN7ZXFB2MD6h25jJgiUFWHYUoJBkky7bg1NFIro8sS5BwiHd3gjUJZlMWuXkaAuBMgm0HzDtKbJQ9zzYcRSgKti/2awurwA7ZTJuewrf121sadZ1CfmBrqhr2tvbi5YH2N9333239ZqYoK3Dn/3Zn1ltHdCGAC0lxsfHrWPzlltuCZSxf/9+FW/evFnF559/vrW81au1HUxHh85JtBwwpTa4H9o3oFXGt771LRXfd5+223FJddAyw7RWwHEbGMNwfCaTsfYPWjzgPmaf4n4tLS3Wc+G1RbsGlz2Ezcph6ty1W2u42m3WHY/H/VKplLUf8DrjdtOeAvfDa97oTFfqOZ3jy8lXZ0qyN12ZXSXHzgWVSn0r2cfVjnJlVNv2aqXM5cqjTNkOc7fy8hb7WCH1B/O38vKYv6SemEmbrOkei7kxUzY107WWqeRYsrjhSmNCCCGEEEIIIYQQQgghCk4aE0IIIYQQQgghhBBCCKnNnmLT1q1eLBbzTpw4qbaNj2n5cB7sCeKx4Kkvu+JyFT+xd6+KM2Ct8BTYZ2RsUsX9pwdVnANJ87q1qwJlfOC/v1PFq1YsU3FzUkukc56WLyfiCV2PjJZHgyLaawK7BqEtoSXdQycPq/hH3/uuivc8skfF6HQAbg1eHnw2TKUAysE9kAignDsHVgyxmK5TDmTe6BaRSQelBnitcgV9THNzi9VCI53KWqUNWHYUHCxMUOngksUHJedp6/7pbNA6IpXS+yUS+npmwW4CLSyClQL7jLCjDw2iMbALiUS9SLgxJBznnHNOcVwdO3bManOAUhTJceSqq65S8eOPP261BXjqU5+q4tHRURX39/dbx+/69esDZXzkIx9R8cqVK1Xc1NRkHR94rbEeaGeAx5r/7u3ttVpSPPDAA9by0HLBZcVg5q7LQgGPwf3xvHgs9pt5rdCOAe0pECw7mLsx5zV3lRd4bwIqaatpszE5OWm1vUDLD7PtNsxr4ALrLu8p5ew5FjrTsTAox0yday6eRD8fVCL1ne4Tryu1sZiKclYTlZyrEa5HI8LcJaRxYf4S0pjUYt9VCTN1rnLnmY7NDCEluNKYEEIIIYQQQgghhBBCiIKTxoQQQgghhBBCCCGEEEJqs6fYunVbURaeB8+FA8MHVYyr3Fs6gk+qPzN4RsW/vvM3Kn7ljTeq+L4HHlLxyVNaPr5i1SprhXddsCNQRjSql9+nJrT0fnR0SMVZsFlAOXg2o7cnElrG3gbWFsKJ/Y+q+Gc3/1jFhw8f1TsVwPbA0xLoQi5vlQSYMmmUW6Ps2/N0+8IQ4z6RsN4ej8ed0nDfB6l/k5b6F2D75KSW+kci9t8XUtCfra1BO4B4XLc3nU5Z64uSCZTao10IVMmbnNTX1TwmGtX9mMlCH4IVCLYjCn4aObg2IejbYjsSSauMX9wtwOGi7u0pnszdvNVqAq9JZ2dn4NgzZ3Tu3nHHHSp+3etep+J7771XxSdOnFDx6tWrrdd6165dTuuAiYkJFY+MjFi3o20Bjm20rTDtGvbv36/iH/zgByo+eFC/h1WSh5i7pjWC6xiX1YVLqojtQLsGE7TcwGuLfeWyb8DtbW1tgdfwvQMtP1y5i9cD64vtwzqZuYsxngvbhO+TOF7wWBO0vcA+lbo3iqRyeHjYa29vr0gmV65NrmOqlcvWYlUwXRaTLNZ1PSq5fuZ+M1WPhSTjrqfcrRTmLiFzT73lb7nzzhbMX9KIdHR0zMg9TbX3Y7Wcd7agJQWZaRpkyosQQgghhBBCCCGEEELIXMBJY0IIIYQQQgghhBBCCCG12VP0rFjuJZNNXhhWth88dETFHZ1a4uzng8vfH/2tlsKnMpMq/vktt6j4xKlTKj73/PNUvBS2b163VsWjY1r6LrS3ajl6R0e3iqNxLUtOndQ2EiGYMk8ktO1AE0iaTx8/FCjj7ttv0/U9flzFWbBACIHbRBgk1RGwrShk3BJ3lGQHnBJCIGvy7DLxeDJhPW84HywjHgbbDJAqjI2B/UNzs9WWARUPPsjV02m9z5PlgyVGSMfxuN4vFtNDMJ3S+4RgaE6m0JIC7TpEph62SzH8kEPWHrbbBGT19YuBbYWQgPqirUc+V/DyZaTx9cTKlSsDUn3hwIEDVkuKwPjzPO+RRx6xWhX853/+p4qPQy5ceOGF1u0bN25U8ejoaKAMtEdYsmSJ1Sbh2LFj1muKtgNo14BlC7/85S+t58L24phACwTcJ2AHY1jL4H6VSINwH7w+rrLNY/Bc2KfYn8HctdvBmBYYrrpjHfG8OC4Q13azjS5bD5clhWt/HC9mfbGvpO3lbC3qUWY3F7LWSqSolZY3nTrW8hToRqbaNpWTRs6GnHgh9vlc0Ki5Ox0WW+6ShUu95e9cwPwlC8laZi4sJerFBqJcftKSgswEXGlMCCGEEEIIIYQQQgghRMFJY0IIIYQQQgghhBBCCCG12VMU/RzCYa9n3Rq1aev2zSpOxrQ0+Ojhk4FDJ8a1TDmb1/GDDz2o4lhS2yEcOqhtL9Igz96ycYOKM4a8OAyWCz7YE2QzWdgOB0AcjupjwwUt1X7wnrsCZTz++D4VT2Z0+bnAiUEO4dktJUxLCgTl1j4cHwlYVej6huBceTg2m0OpfLAMl4ghAXJu7M8s2DfkwYojDhJ1KLrI0NCoVTbR2tZsld5HQH6ezejtmYy2rYga1hHYj/G4Pj4KsvYQ9NXkpD5XPjdhrV8kEiwD+zSU1fVqam3xCg2i+JB+kr9169apbeeee65V1n/48OHAsWNjYypGSf+9995rtQHYv3+/9dpt3bpVxdls9qz62WQ0eLxLIogWBrj9zjvvDJTx6KOPWu0YTPuHqTAtKRDXudBaAccathuPrcU6Aa058LzY13hevOb4niMMDQ1Zy8AniaMtCFpV4DXDfsZ9zDpiXVz9i1YXLhuQclY/WC+x72g0udZ0pXXVloFMV5I7XSiLnb4ksZJrWOl1rnb8zaT0unSukZER6xPS6xHmLiGNC/OXkMZkLvKnkvub+fi+0WjfcUj9w5XGhBBCCCGEEEIIIYQQQhScNCaEEEIIIYQQQgghhBBSmz3FZO9Jz08kvJVrtT3F7+28UL/equXR/b0/CRybyWqZchok/l4YbQtaVHzRbn3ei3dfpOI22Ke1RdscmBYKaMCAFgKxaNzqTxGP6vnzkf5eFR85fChQRgpsEybB9iIcCVvPi9YG6I2BsgFTGo6EoH+Q4PH2OCBNMGQZKFN3SdkDbhgOlQNungT5+O8qoMJ4HGws4PL7BV1KHl4YHR2z1g9l/ua/I3ANCp4+VzyqZfTZ7KQuD+w7sLWxmFvC0oIWAKFw8a8R6O/vL9oJrF27Vm276qqrrNYBvb16/JsWAyjxR+kN2hZceumlKr7sssus+7S2tjrHI5aHY9i0N7BZG0g7Sxw8eNDZDrQ6QDsEzEWXRUSlueuyoHEd77LoMCVVmKNYd9PyoxomJ3VemOW7bCxwH+wreWqxrU2m7QReT5fFCFpgiBx9KhsQrKtJc3PzWXYtjUqlsrdq5XizJWer1hqBzN04qaTfZ3KMVbq/q+6NLpdutNyt97IJmUuYv4Q0JrNlvzWf9ySNfj9EGovG/dZMCCGEEEIIIYQQQgghZMbhpDEhhBBCCCGEEEIIIYSQ2uwpDvziZ148EvESYB2xJqdPse7KS1Tc3a5l/ELW07L2RJOWHLd3tKn4955/nYp37tJldDfrfTradRwKWEJ43uTEuP4H2B7EQLLc0qol8iFPS61TwwMqfuT+B1R84sSpQBmpHMizAzIlvY+fBwkEVBHrm89rKblvSNyxvgHbCygPFQnZLB6v9w+Bp4QpYQhBxVByHgXJePCIqa01otGg/ByLxBil7CgzD1hjQP3QgiKZTAbKQFl7GqwT4nGQu6OJBpw3FEbrEHvfFssAGXsTll8oeCHfbU9QT9x+++3FvrrkkkusUv5du3apuLOzM3AsXmPs/yVLlqj4xS9+sYp3796tYny6PcamNcDExIR1rGId29p07iNDQ0Mqvu+++1R87NixwH5orYHgeHaNbRxnOGZNmwS0UzDtGGztc9UJMfsK/43lo92DS0Lpzt3gRwEejzHm7tjYmDV3sX5YJzN38TW0DsFr7pKTuSRZ5na8Bk1gLSNtbxRJpth9oLVLNZK0ap/qPJNSt+k8OZqSu9mlknEx39enUfJzIebudKiXehAy2/lb7jOO+UvI/IHfN6f6LnT2/MjUFmrTub+dLeqlHmThwpXGhBBCCCGEEEIIIYQQQhScNCaEEEIIIYQQQgghhBCi4KQxIYQQQgghhBBCCCGEkNo8jYf7er1YOOw9fmdKbetralbxavAQ3bluZeDYTRdsV3FmYFDFS0a1D7F2Mfa8rSu6VdzcuVzFYbAQnchNBsooRLQXZiqrPTKjEd3Mtjbtc5MaO63iB++8TcUP3He33ieTDZZRsHvdBrxCwUM3DM7AIdgnHEbPnKD3aSGHnqngoYxHQJvQszcUyjp8Sg2vG6hLLBax1iuX1/6lPtQj7Gh3DuotxMGzFPdLpVJWX9VEQnueoo1rBHydfcNDOBoF/1MYzvG4LhtJJvX+WbCTDaFnM3hdF88FPrUJ8GXNplNBz+k6pr+/v3jdfvOb36ht6NV29OhRFa9bty5w7AUXXKDiM2fOWP100T8Kj8ftOAZML1/0xMUYPWmxviMjIyrGNt11111Wn1xzrOEYxu2VeFbhdtMLuJzfsa1NrvOi3y/WzwT3w/NiH1ZSd/QqNs+Lx09OTk7pdY1luHydzdfQxxhjBD2JcfyU8xZzeSrL8a7r02jebI3gQViJRxyZXRphnCy0ui+E3HXB3CULnWryt9FymflLFrMfucvfuBFotPcasnBojBkvQgghhBBCCCGEEEIIIXMCJ40JIYQQQgghhBBCCCGE1GZPkSmEvYIX9tJgQ3BqfFifbEJv79mxOXDs+pY2Fe/7zb0qjvT2qvihY0f09ja9f275MhUvW75CxV3dervQ0tKqYj+j58PzGS29Dvm6jo8/8oAu+4H7VVwooFQhKANA2wSccc/jIQWQZwck4FoyjspoU36OlhR5eC0Ehce1Y4IXDqHUXse5HBwb8p3yhjAc40P/oBMEqjewD1Hmb0omsO7Yp7mCvh4xsNnAw7FPQiEtY8+gp0SxH/TxsZiOUXqezeas1wD7MxbVcSQSbEdLq7ZgyefBRiGX87KGJUe9IvYDcq3QhmBoaMgq91+/fn3g2OXLtT3MY489FpD/2M7V0tJilfetWKFzd9myYO62trZaLRBcNgsPP/ywiu+7776KrBzKjVXb8WhtgP2G+5jlufbDshNgd4L1cNklmHWtxFqjEukV1tUsw9VGrCPaW7jagX1oWpJgP+C58Hi0GMH6Yh/gsaYdBsrT8LxSFxxbjUYl1ikmlexXiUxvutI4VxmU3DUOMzWWprN/o8LcJaRxYf4S0phUYj9oUsl+leTPdO9vaPFG6gGuNCaEEEIIIYQQQgghhBCi4KQxIYQQQgghhBBCCCGEkNrsKdadv8OLx2Je7lSf2ra0SUvR155/noo7NgUl7ntv+42Kjx3ep+IWkBY3r1yl4l/eqy0sDvz2cRU3NWn58XJDRn/BrktUvP68c/ULUX3MEbCkuPtXP1fxxISWQefBayKRBB8IkWqntaS5AHJpz3fI3cFqogDnzYNdA9onnC1DAGkEbgV5Np4rB/XzwEYiFg3WL5GI62PAYgHl3b5vLzsKZaP9RiQS/A3CJZ0PQxmhsC4jlUpZZe1YdgqsC57cT5dfAHX/xMSkQ6oPx0ahDGhTOBaUuOfBTgMupxeOhL2wYWVRr5x//vlFGf/AwIDa1gYWMPJ6iY0bNwaO/fWvf63iQ4cOWa/R5s3ajuaOO+5Q8Z49e1Tc3KxtPtatWxco45JLdO7u2LHDakOA57rttttUPD4+brUwaGpqCpSBthe4nwu0M8AYx7VpuVCJhMgld8JzYRlovyAkk0lrOzB3XTYdeC68fqatgzN3MU8gxr7F8yITExOBf+N+WB5eT1c7XHU/yyLHcd3kGKx/IzwFuhYJaaXy2Wr2qVQON1OyPjL/mNepEvubat8Ly5XRqDB3CWlcmL+ENCYla8Ra7iMqta6oZp9K8202rL8ImSka41szIYQQQgghhBBCCCGEkDmBk8aEEEIIIYQQQgghhBBCarOnOOd5zytKvtcu6VTbJo8fV/H+/dpG4sBPbwkcmzzSr+L2mJaNpzu0RL7nssv1eXPaqmDkngdVPLT3sIr37Xk0UMbJx7TtxbWveIWKH777ThWfGTiizzU2pGLf1/Pnuay2qsiC5UERVEuDRAClz4XC1PIClJUXAgYMQeuJCMR43tRk1mG5ADJzX9cpBDYO5jEodUA5dygEvykEmgH/qFLGYbYjm9f9gGrxgGwfLDfyhWB/Tk7YJf0BW4+AdQjagujzorNGwSgjk9ZlhKFecm2gi+uaZz3rWUVbg2XLlqltvb29Kt67d6+Kb7klmLunT5+2WiOg/cPu3btVnE6nrZYS+/bts8Zm+a+A3L3zTp27p06dUvHIyIj1uuM1Na0NXOPTZcXgskYoVwbaP2CMeYU2LFhewJLFYQlhvubO3eqkh5XmrstOA+uIfeJqq2lpgXWvBCwb7SlMKw/TPgTr67q+jSazmy3pbC02ArU8WZ40HtO5nnMh9a4XmLuENC7MX0IWprXMbNlWuPYx968kX2lDQeoNrjQmhBBCCCGEEEIIIYQQouCkMSGEEEIIIYQQQgghhJDa7CliyabiX8+2c9S2C573eyq+88c/1fF//DBw7Gj/sD7PhLYkWLV2rYqPHT2g4nzfgIrXhvVy/WawX+jt1NYWwtLzdL2yI6MqPvngQyoutMM8OZwrNa5l1F4erCaMeXWUUedzEIPUORKxdyvKoVExYYonItDeENosQL1yeS1biII8G/ePxuLWfZ6sY8QaB+sLcnfYjmW4LCGK/wrZpfMoZccjopGYw14C6h3GfYLWExjnoa+CdQocDZFvtQ4x98PKtDQ1W65efRKPx71EIuFt3bpVbXvRi16k4h//+Mcq/sEPfhA4Fq0gsG82bNig4oMHD6p4cHBQxS0tLdYYrRiEbdu2qXhsbEzFjzzyiNUOA8cQWiDgODOlPfhawB4GxrBpBWHbB+VD5v7uXCpY6+HaH/vHtFzAf1dSnotyfYVtxL5ylefaXk4mWYnNhqtOrvOcnbv2/ZqbmxtatlmphM3VxulI9iiZI3PBQpVpMncJaVyYv4Q0JpXatrjybDo2NY38fYOQElxpTAghhBBCCCGEEEIIIUTBSWNCCCGEEEIIIYQQQgghtdlTROMxL5qIe1mQACdaRKL/JM98yYtVvOuaqwPH7n9YW0Qc2vNbFZ84eVLFd997r4rH9+1TcTdIrZs7tCVFbP2mQBnbd+xS8X03/4eKI626mamIlhdEwFogEdf7TE6kVZwHiwbB9wtWS4OgZDw2pVw6ErJbShTbhcejVUbBIU1CqwuIsU6hsCE/D/wLZeK4GaQcDtk+jgXsT/O8lTwpNBRGibvdauJsibs9RtBCAMuOgD0JnjefD5YRieh/pzNZFSfiOS/nkNLXG2JNIX/ZbDYg0S/xkpe8RMVPfepTA8c+/PDDKv7tb3XuHjt2TMV33nmnig8dOmQdg52dnSru6ekJlHHBBReo+Cc/+YnVkgLB6yXWGyUmJiacNgcuaRHWEceKM3cdVgzmubB8l12ES7IUyN0KZU1YRmCcO+qLY8Fly2GW77LycB2PfWC2A881U/1jXnNsezqdDowZlw3GQpLLViJnLfd09mrLr2Q7IdOhNKbFNqmjo8NrZJi7hDQuzF9CFp5VRSX547LXq6X8SrYTUg9wpTEhhBBCCCGEEEIIIYQQBSeNCSGEEEIIIYQQQgghhNRmT9GSSHrNiaSXjOnDcmDfMJHW0vDR1GTg2KWbt6i4e8s2Fa8/3a/iji2bVXzq2HEVHz18QMUjY2MqXrl6daCMgw89qOKxwT5dxyaQV8P+YfhHCOfPUXIUOCJoSREKha0S+UwmN6WEAeXcpj0FSrXR/gGtEMJh+6WLRPV5C77e388VnGWE8miVoYmBfQNaZhTAoiMg8TYkHdhGPMbzQZLlTY1LEm+Wj/vhdpSou2T02LfQ7Cer60esFiFjY2MBu4p6Rqwpkslk8b+2PkJbB4yFjRs3WuOBgQEVb9iwwWpbceDAgUB/uewpHnpI29ecOXNGxbGY3eplJiU8wdzNWPcJ5AuMc7RJMPsUz4V1d9lF4LlcVhNmGa5zYb/hPngulyWEmScui4lyx89l7pbrD1d7R0dHnde6kalUljpdaR0hc8likFszdwlpXJi/hDQmlebhdO1kCGl0uNKYEEIIIYQQQgghhBBCiIKTxoQQQgghhBBCCCGEEEJqs6dY2d3ltbS0eJvWrlPbImAwEAnp00VBHi2MjWhpej6vpQAol7/osktU3P7sZ6q4GeTjg8P6PH4qFSgjPabl8j/MDar4iYP7VRwO6XplclmbY4LspMPAC57n5/W/Y/GYQwaNsmu7FCkQB0oIvpbN2e0fwmG7TAK61suCTQbsXmqYClHRHYvo3xHCYbBlgPPmA22C6kWMvoLXAhJ52K0AO6VBLo5SdB9k5Si7r0YKbwPtRQoFGAuGxD0onceO8LxctjHkZStWrCjmLtpI4LjB/jL7WJ5Sb7NZwNy94oorVNzZ2aliscQoMTQ0pOJ0Oh0oQ+wCSnz9619X8d69e631xXogLisG08YA6+6yMHDZL1QqZXLV0SVxwvOWs07ANrrGfLXtM6VW+JrLAgPPi9cT98c+RxuQ6eauyz7DrKvL9kJi1/WpN4aHh7329vaK9jXH5lw8Xb3cU6gJWcxUk7uVwtwlZG5g/hLSmHR0dMz4OWfSTsb13Y+QeoYrjQkhhBBCCCGEEEIIIYQoOGlMCCGEEEIIIYQQQgghRMFJY0IIIYQQQgghhBBCCCG1eRp3L+3yWltbvba2VqsvSyarfWFjUe2pKcTj2qNzIjWh4ryvPSdzGX388Gnto9qydImKVy3RsZcP+qLu6dX+p6Mp7X0cjetm+gVd31RWe1pCNQJ+mehJfLbfK3qsZqf0Lg56meJpgnP345OT1jKiUV12BOoYiej25bJ2H1bs52K7oFnJpPYBDUX1eUMuD2aH/w5YOT9Zr6hv9yiGuhTA0dmHSuV9u09p2PDtwpoEy9CvoHdp0C8V+9Dukfpku/S/o1CXRFz8eu1+r/XG0qVLi7mL/myB3AUPXdPTGL1osS9dHrxnzpxR8fLlywN1cPkFnzx5UsUTExNTelijh67LD9f0eHOdy9Uml6exywtaGB8ftx6DfYjHRKNRaz3MMYhk4X22qanJei6kkjaZ2wM558grV+zyGDb7Co+pPnftdTX7zVUX8dquxDu5npgLz0J6JBIy8zCXCGk8ZsMXlRDSmH7khCx2GutbMyGEEEIIIYQQQgghhJBZhZPGhBBCCCGEEEIIIYQQQmqzp2hpa/Fai9YUICsORazy6AGQqJvyPLRZiIK8vwDbPZBLF8DOIJNL6bIjQckfWkmMjmh7i0wKbCzCUau3QR5k0ChjRtuC322xSqdR3p3PF6aUsuP2bC4oqY6E7PsFLCkccu58XtcJG1iAOhXPC30XqDv0YQaPgTqipUQkDP1R0BYFQhR+kwg56ot2E7gPqjlxn7whoy845NRoKYESddwHHTsC9h2GkjSK4wyuTTaX9rJgS1LPiEynra0tsM1lkzAwMBDYD/sMbRbwOuJ27G/cB20VMMfMMSiyohKpFOS7w07AZU/hsmsw6xLMXbstBNYX+wPfA8zXXLmP53JZMSBmnYL2OQVr7DoX7oP1QHsRs+9c5bnez1zbzXa4rBDyleSuo99MGXjwvTwSuP44BhoZl13QTB9DCJlZeTvtYAghhBBCSL3DlcaEEEIIIYQQQgghhBBCFJw0JoQQQgghhBBCCCGEEFKbPYWo9OUvA5L8sUktHx8c1pYQKUPuHAZLgwTYUDQ1NdllzWCTEAbrAN/T8VDfsUAZ9915m4rHx0b1MeA8kEpPQqXslgmIKRkMygnxVFr6HI+HrfLqMFhd5LI5h6WEIcMvoNwdjs9jPUAyHrBo0OcNh4Lti0aidlsHaF8qk7VbSkD9snA94tGg5UAIrEvyUF8fLkgYjsGyIz7WCS5g2LQkQbsKKAPGT3B/sPLwQPoOXW5aJ4Shr/B6Frycl3HYANQbco3lDyX54+PjKh4aGrJaQpgkEgkVNzc3T2m54OLUqVOBf//qV79S8ejoqPVck5OTNVsmmOdy2Sm4bDZcVg6mDQTu57K6cNkplLO9QGKx2JSWDel0esrcxTJMKw/cz2U3UoldCGJeD1f/uMaPy6oiYOFj5C62y7TyKNfH9ShxrySvykncKYUnZP6hNQwhhBBCCGkkuNKYEEIIIYQQQgghhBBCiIKTxoQQQgghhBBCCCGEEEJqs6cIe6Hi3wTIxIdGx1Q8Mjah4pzxZPolbW0qbm1tVXEBrC7k3CWSIMFOZbRcPu+D/BwsIYRJsKRA+4csqKWzObSL8BzScLsM2pQWotw5KJe2nxfPFI7qdiSjyUAZ2awuH50rsE3g3uBFwPYCLRoiUHYspmX3xbrDfngMSrZdUnZsCbY1BrYjwbMG+wr7MIf2HYE+jDgk+EHpex7PizYFDvl64Pqh1B461LzmgfEQaJWU7zUEci3lb2JC5+jw8LDVEgItLMwnwLdBHrtsFtA+wWV1Yfaxq3wcN2gLgbYDrnFq2ifguVzWCC7bAwTLNm0dXH3iskIwj7fZLGB/lqPasrF/XPWw2TrYysBr5rKEMAlYvTjfS+3WEy5LEhNzLCONYk8x1XgsJ3enFJ6Q+oLWMIQQQgghpJHgSmNCCCGEEEIIIYQQQgghCk4aE0IIIYQQQgghhBBCCKnRniIcLf5FwTqgNdms4izIfYdSWgb/JFqS19zcpOLBMxNWKXs6IJ0GG4GIlmqPgx3Gk8enVZwH34CCjz4UMas1QsDCoADbw0EpYSgE8+whl4Tb/pT6HNTJR5sDPyiVR7uJUEjv53sgyQapdh4sG9C+IRrTlzcHbXry+LBd6u2w0whaPGBdAx4fgTL8Ahxj2DrosvX2LF4PsO+I4DWA/U1CMGZQlh2Qg0IYPBNaHAQl9dhG7N+QHwrYhzSCPQVK+VtaWqxS/UmwnzHBYwYGBqy567I9wHh8fDxw3nQ6PaUdQiU2FOVsC3AcuI5x2Vbg9nKS/0A+AHgM9oPrvGhJYdoouI7HNlXSVtxezsrDhavfsGysa7l+w/Jxv3LWEzZc189Wfrl9FyIum57pHku5PSG14bxPmYPy5qpMQsjMwPwlpDEpZ3VKSKPAlcaEEEIIIYQQQgghhBBCFJw0JoQQQgghhBBCCCGEEFKbPYUn1gyhsBcDCXYqr2XpPcuWqTgZTwQOHew/reIV3d0qxgX6aKEwAXL3THpMxUs7u1R8769uC5Rx6vhRqKuWAqTSWX2ugl0uUACfAaxT1JCbx2JgVQBz7j5YTBRAduBSIITB5sK0wAjK83W9omDZ4MM+0cDxUWv70ObCLD8A7gZWENgP2L5gHwbl3uks1j06peQcz5tDy4283h4pI6PHPnHZBATRdc+jBYAhIynAuAxDX8dCYS/ssN2oN+Q6yR/aHkxMaHuXFStWqDiRCOZuf3+/ipcvX249P0r98bxoO9HVpXP3ttuCuXvkyBHrefF4l52Ay0rBHGfxeNx6jEsm5NpezjIBj6nEbgLzwoU5lqu1bHDZULjsPsz+wTq68splKYLnNY91XYPKctdz1t1VRjk7jnpmeHjYa29vd0pRp2M7Ue54Sl8JmV1o+0IIIYQQQuqdxvnmTAghhBBCCCGEEEIIIWTW4aQxIYQQQgghhBBCCCGEkNrsKQZOn/ZS6bS3besWq6VAHlTiq1euChzblmhScTqVtkrwEomk3g7y4cExbU+RPT2s4pVr1wfKCIN9Qzyuz9sU1tJwP51RcS6nKxwKoW8FWDFgo+QYD2wowJoArRkCqkK0hUD5oa/rmjfk9dmsttMIoY0F2ClgGAk75NwOe4liOxx2DEFJJJYNVccioO4yNpA4XA8EiwjIx6EU7M9cGdsJpzVBNGLtQ5SAuo4NnSXx1vWKRvVYCoUjXshxjnpDLCYmJye97du3W+0esF9Wr14dOLa5uVnFKbCNCeZuwir9HxkZUXFfX5+K161bFygD7RswRrBsHL8uGa95fTOZzJT7VSIJdllQlCsDxy3GTquWMmMey8DXXHXHMlzjH21AytlmBOxoHNYPLnsKs60uK4VK2uSyKim3n2mz4cr/RoZPaCakcaxlENpREEIIIYSQeoQrjQkhhBBCCCGEEEIIIYQoOGlMCCGEEEIIIYQQQgghpDZ7ipP9/V7LxITX0tKqtnV3LVFxBGXbOW2xIHR1dQZsLkpMZvR+MZD+x+PaqqKjvUPF42BV0dWzMlDG8tVr9X77D8J59T4dMW2Tkc1o6TKEXgZsKwq5oAy6UAB7CrB/CIO0MCCdj4A0HNSH4YBMPCghjkSgwtinKFkH+4UY2EAE5MgxsMDIG2WAVLsSqTfadPhhtLNw/+4AXeVlCnCd8YKALUgE+ipoKaF3z2bNuoIFB/QJ9kMeLUUCFiFQDYjDRpuC9QJ5fvF/jSH/PnXqlDc2Nua1turcXbp0qYpRqm+Oh66uLhUPDAxYLQ3QUiIej6u4o0Pn7ujoqIp7enoCZaAlxt69e61twDLQwgXzDbeb7QiMCXgN7RDwXKYthA3T4gAtEFzl4T4Y47lc283+rSR3XRYR5eTQLusSl90B1hfPi/vjtSl3bV39Vkk9zDZhX5nWGo1mT4FtrkXKXq1Vhau8chYYlNgTcjb1kiPMXUIaF+YvIY2Jef/t+p5ESD3DlcaEEEIIIYQQQgghhBBCFJw0JoQQQgghhBBCCCGEEKLgpDEhhBBCCCGEEEIIIYSQ2jyNQ9FI8W9gcFBty4BP5eqV4DFcxr8lCv6VefSpzGRUHEmAL2qL9jduSegqp4dTgTL8kH4tk7NXBX2FEwntbzwxrL2S09AmH7x8i/VC72LwYw6HI9Y+QSvhWCwBddJtLWdFhT7PUfDWjUTxIPQ1RQ9kvb/p3pnO6vLDUEeXlyp6AaPnMl5X01815/Qg1cck4Hqi32o+0O/o/RNsCZafQ/9pKC8c8GDG+hasPsZJ8EY2jwmMJT8f8LiuZ6Rv5e80+Imjx+yqVasq8l5yec9mIHcTCT3O29raVNzUpPNteHi4Ig9el9cTljE5OWn1WS7XDqwvjjuXJzKWh9vxWBPsK5ePsQvMPbMd2EZXea7jXb53lXgjmySTyYreB2z1MI/BfnfVN/j+kLfug9fJfM0sv1F8xNAXvFpPZ1f7XeOAvoiEzF3uMt8IIYQQQki9w5XGhBBCCCGEEEIIIYQQQhScNCaEEEIIIYQQQgghhBBSmz1FcYo57HmRpLaOSIGseHxyQsVLDFkeimnjICFubtaS9cnRcRVns1qinojrajYltCQ60bEkUEb30uUqzmT26BdCYDcAFg/5jJY450HOHfCLAGuD4uF4Lq0G9yIgOQ9Hw1Y7BXBA8AqFyro+hrL2CEq1dY/mfN1XBfDDKNgdIX5XL3gRK+YAuwSPRSsBlMT/bkcVxkE6H4FrgLYXYYhRro7nLSe/jkZ0GVmwngiF9HnzBbttRTwO8nrDAiOc1/XNgRVIKFQwbDTq354iHo9bbQ4mJiB3lwTzCkH5f3Nzs4pHRkYCZZXA8lx2BsKKFStU/OCDD1r3c1kguOwizPEYiUSs9cLtaB3hkhFXYi9h2kVgGXgul41EOesEfK2cPYatPDwW+61cedgOl0UEbkfrj7PeEwA8BsvA3A/az+SmvJamNUY5G4ZaLDnmE9d4rMVmgxJ5QuaHai1jCCGEEEIImU+40pgQQgghhBBCCCGEEEKIgpPGhBBCCCGEEEIIIYQQQmqzp4hFIsW/BMizQ4WwwxohKJnNg+Q4GdcS9672ThX3p/U+Odg/D+dNgLVBR2d3oIzV6zer2Pd+ro8vaBlyJAzy84KWO7c3a+l8PuSweBDpdRrsGLQK2yuAAUc4ClJ2H+wsQH2YB3l13pAlhuFcIR9k+DmQsof18ZGwlnbnwaoCrTEC/iC/u5a2uuc9fUwYKuzDNfDzUAb87ODDtSkej32NMnO4HlGwxijkUYpul+1HwfpDyGRgzITwOqNFCNQJ7UkKcJ1isN3oqzDU18c+8QteNtsYEneR88sfSvmxX102CaZdANpToI0FWhLg/mgBgLYOXV1dgTI2bNhgrTdaEqCFAdaxtbXVeqzZDrTjcEn6XTYS0wXbgefF8lzXwwT7wWUx4bJ1cGFabmC9EFe9cH+XZY1ZBo4ZPAZtK7BNWAae1zUuBJcFhexXSb/UA8PDw157e/u0x+N0LS0IITMLLSkIIYQQQki9w5XGhBBCCCGEEEIIIYQQQqpbaVxalTQx/uSD6poS+uF1IVytCQ8Wi8BD24rngJVhkYgudjKlV/+Njo6qOA+rwDIxeABWHB6ylIMn0Un9JidVnM3BCl1YVFXwctY6FaDu5VYa43lzEOMqYnlAmirD8TC6wOreMiuNC7haGOqLz7/CGOuUL7OaDB/2hnXBlS+ulcYIFmGuXsPn6+FK48B2x8O/sJ+xELMW2F584F0BHhQYWGkceAAgjF148KIHq+eLx8PYwIfnPbnSOFfXK/dU7v7uQXdjY2NnvWauAjVXhOKqTlztmUqlrLnrWh2MK0vNFaCTkLt4PO5X7QOEyq06dZ13tlaBVVJfrFO5lcauh9khrocGuijXVle/ux6I6WqHWYZrlXSlDwS0lW1S7sGIpePqPXfxIZM2pnp9po6Zi3MRUi31mL/1WCcbzF0yn9RrntRrvUyYv2S+aPT75nqikepKGpfSOJtO7lY0aVyaELrh+pfUXBAhCxnJkY6ODq/eKOXuy172svmuCiF1Sb3n7tq1a8vuV0vdZ7K99dh3ZPFQj/mLP6LWM/XWb2RxUY+5KzB/CVnY9831RD32I1m4jE4jd0N+BVPOskLrxIkTXltbGz3YCAEkfSQBV61aFVjVWC8wdwmxw9wlpHGp5/xl7hLSmLkrMH8JscPcJWTx5m5Fk8aEEEIIIYQQQgghhBBCFgf19zMRIYQQQgghhBBCCCGEkHmDk8aEEEIIIYQQQgghhBBCFJw0JoQQQgghhBBCCCGEEKLgpDEhhBBCCCGEEEIIIYQQBSeNCSGEEEIIIYQQQgghhCg4aUwIIYQQQgghhBBCCCFEwUljQgghhBBCCCGEEEIIIV6J/w8A3ejQnVR0RQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1800x1800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import PIL\n",
    "import PIL.ImageOps\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "titles = ['dog', 'elephant', 'giraffe', 'guitar', 'horse', 'house', 'person', 'dog']\n",
    "plt.figure(figsize=(18, 18))\n",
    "\n",
    "original_img = Image.open(f'work/PACS/src/0/0.jpg')\n",
    "original_img = np.array(original_img.resize((64, 64)))\n",
    "plt.subplot(1, 6, 1)\n",
    "no_axis_show(original_img, title='original')\n",
    "\n",
    "gray_img = cv2.cvtColor(original_img, cv2.COLOR_RGB2GRAY)\n",
    "plt.subplot(1, 6, 2)\n",
    "no_axis_show(gray_img, title='gray scale', cmap='gray')\n",
    "\n",
    "canny_50100 = cv2.Canny(gray_img, 50, 100)\n",
    "plt.subplot(1, 6, 3)\n",
    "no_axis_show(canny_50100, title='Canny(50, 100)', cmap='gray')\n",
    "\n",
    "canny_150200 = cv2.Canny(gray_img, 150, 200)\n",
    "plt.subplot(1, 6, 4)\n",
    "no_axis_show(canny_150200, title='Canny(150, 200)', cmap='gray')\n",
    "\n",
    "canny_250300 = cv2.Canny(gray_img, 250, 300)\n",
    "plt.subplot(1, 6, 5)\n",
    "no_axis_show(canny_250300, title='Canny(250, 300)', cmap='gray')\n",
    "\n",
    "canny_reverse_250300 = Image.fromarray(canny_250300.astype('uint8'))\n",
    "canny_reverse_250300 = np.array(PIL.ImageOps.invert(canny_reverse_250300))\n",
    "plt.subplot(1, 6, 6)\n",
    "no_axis_show(canny_reverse_250300, title='Canny Reverse(250, 300)', cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some Module\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 这个是对先验知识的利用，已经知道目标域的数据是手绘风格，因此在使用源域数据之前先做边缘提取，并做颜色反转变成白底黑线\n",
    "class Canny(paddle.vision.transforms.transforms.BaseTransform):\n",
    "    def __init__(self, low, high, keys=None):\n",
    "        super(Canny, self).__init__(keys)\n",
    "        self.low = low\n",
    "        self.high = high\n",
    "\n",
    "    def _apply_image(self, img):\n",
    "        Canny = lambda img: cv2.Canny(np.array(img), self.low, self.high)\n",
    "        return Canny(img)\n",
    "\n",
    "class ReverseColor(paddle.vision.transforms.transforms.BaseTransform):\n",
    "    def __init__(self, keys=None):\n",
    "        super(ReverseColor, self).__init__(keys)\n",
    "    \n",
    "    def _apply_image(self, img):\n",
    "        img = np.array(img)\n",
    "        img = Image.fromarray(img.astype('uint8'))\n",
    "        img = PIL.ImageOps.invert(img)\n",
    "        return np.array(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# TODO\n",
    "# 这个是实现VADA可能用到的工具\n",
    "class ConditionalEntropyLoss(nn.Layer):\n",
    "    def __init__(self):\n",
    "        super(ConditionalEntropyLoss, self).__init__()\n",
    "\n",
    "    def forward(self, x):\n",
    "        b = F.softmax(x, axis=1) * F.log_softmax(x, axis=1)\n",
    "        b = b.sum(axis=1)\n",
    "        return -1.0 * b.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# TODO\n",
    "# 这个是实现VADA可能用到的工具\n",
    "class VAT(nn.Layer):\n",
    "    def __init__(self, extractor, predictor, n_power=1, radius=3.5):\n",
    "        super(VAT, self).__init__()\n",
    "        self.n_power = n_power\n",
    "        self.XI = 1e-6\n",
    "        self.extractor = extractor\n",
    "        self.predictor = predictor\n",
    "        self.epsilon = radius\n",
    "\n",
    "    def forward(self, X, logit):\n",
    "        vat_loss = self.virtual_adversarial_loss(X, logit)\n",
    "        return vat_loss\n",
    "\n",
    "    def generate_virtual_adversarial_perturbation(self, x, logit):\n",
    "        d = paddle.randn(x.shape)\n",
    "\n",
    "        for _ in range(self.n_power):\n",
    "            norm_v = self.get_normalized_vector(d)\n",
    "            norm_v.stop_gradient = False\n",
    "            d = self.XI * norm_v\n",
    "            feat = self.extractor(x + d)\n",
    "            logit_m = self.predictor(feat)\n",
    "            dist = self.kl_divergence_with_logit(logit, logit_m)\n",
    "            grad = paddle.grad(dist, [d])[0]\n",
    "            d = grad.detach()\n",
    "\n",
    "        return self.epsilon * self.get_normalized_vector(d)\n",
    "\n",
    "    def kl_divergence_with_logit(self, q_logit, p_logit):\n",
    "        q = F.softmax(q_logit, axis=1)\n",
    "        qlogq = paddle.mean(paddle.sum(q * F.log_softmax(q_logit, axis=1), axis=1))\n",
    "        qlogp = paddle.mean(paddle.sum(q * F.log_softmax(p_logit, axis=1), axis=1))\n",
    "        return qlogq - qlogp\n",
    "\n",
    "    def get_normalized_vector(self, d):\n",
    "        return F.normalize(d.reshape([d.shape[0], -1]), p=2, axis=1).reshape(d.shape)\n",
    "\n",
    "    def virtual_adversarial_loss(self, x, logit):\n",
    "        r_vadv = self.generate_virtual_adversarial_perturbation(x, logit)\n",
    "        logit_p = logit.detach()\n",
    "        feat = self.extractor(x + r_vadv)\n",
    "        logit_m = self.predictor(feat)\n",
    "        loss = self.kl_divergence_with_logit(logit_p, logit_m)\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "IMG_EXTENSIONS = [\n",
    "    \".jpg\", \".JPG\", \".jpeg\", \".JPEG\",\n",
    "    \".png\", \".PNG\", \".ppm\", \".PPM\", \".bmp\", \".BMP\",\n",
    "]\n",
    "\n",
    "def is_image_file(filename):\n",
    "    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)\n",
    "\n",
    "def pil_loader(path):\n",
    "    with open(path, 'rb') as f:\n",
    "        img = Image.open(f)\n",
    "        return img.convert('RGB')\n",
    "\n",
    "def cv2_loader(path):\n",
    "    cv2 = try_import('cv2')\n",
    "    return cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB)\n",
    "\n",
    "def default_loader(path):\n",
    "    if get_image_backend() == 'cv2':\n",
    "        return cv2_loader(path)\n",
    "    else:\n",
    "        return pil_loader(path)\n",
    "    \n",
    "def make_dataset(root, label):\n",
    "    images = []\n",
    "    labeltxt = open(label)\n",
    "    for line in labeltxt:\n",
    "        data = line.strip().split(\" \")\n",
    "        if is_image_file(data[0]):\n",
    "            path = os.path.join(root, data[0])\n",
    "        gt = int(data[1])\n",
    "        item = (path, gt)\n",
    "        images.append(item)\n",
    "    return images\n",
    "\n",
    "class ImageSet(Dataset):\n",
    "    def __init__(self, root, label, transform=None, loader=default_loader):\n",
    "        imgs = make_dataset(root, label)\n",
    "        self.root = root\n",
    "        self.label = label\n",
    "        self.samples = imgs\n",
    "        self.transform = transform\n",
    "        self.loader = loader\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        path, gt = self.samples[index]\n",
    "        img = self.loader(path)\n",
    "\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        \n",
    "        return img, gt\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "source_transform = Compose([\n",
    "    Resize((64, 64)),\n",
    "    RandomHorizontalFlip(),\n",
    "    RandomRotation(15),\n",
    "    Grayscale(),\n",
    "    Canny(low=170, high=300),\n",
    "    ReverseColor(),\n",
    "    ToTensor()\n",
    "    ])\n",
    "\n",
    "target_transform = Compose([\n",
    "    Resize((64, 64)),\n",
    "    Grayscale(),\n",
    "    RandomHorizontalFlip(),\n",
    "    RandomRotation(15, fill=(0,)),\n",
    "    ToTensor()\n",
    "    ])\n",
    "\n",
    "eval_transform = Compose([\n",
    "    Resize((64, 64)),\n",
    "    Grayscale(),\n",
    "    ToTensor()\n",
    "    ])\n",
    "\n",
    "source_dataset = DatasetFolder('work/PACS/src', transform=source_transform)\n",
    "target_dataset = DatasetFolder('work/PACS/tgt', transform=target_transform)\n",
    "valid_dataset = DatasetFolder('work/PACS/val', transform=eval_transform)\n",
    "test_dataset = ImageSet('work/PACS/tgt/7', label='work/PACS/tgt/student.txt', transform=eval_transform)\n",
    "# test_dataset = DatasetFolder('work/PACS/tgt', transform=eval_transform)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T05:45:40.010150Z",
     "iopub.status.busy": "2022-11-26T05:45:40.009586Z",
     "iopub.status.idle": "2022-11-26T05:45:40.017233Z",
     "shell.execute_reply": "2022-11-26T05:45:40.016081Z",
     "shell.execute_reply.started": "2022-11-26T05:45:40.010107Z"
    }
   },
   "source": [
    "### Model\n",
    "\n",
    "Feature Extractor: Classic VGG-like architecture\n",
    "\n",
    "Label Predictor / Domain Classifier: Linear models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "class FeatureExtractor(nn.Layer):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(FeatureExtractor, self).__init__()\n",
    "\n",
    "        self.conv = Sequential(\n",
    "            Conv2D(1, 32, 3, 1, 1),\n",
    "            BatchNorm2D(32),\n",
    "            ReLU(),\n",
    "            MaxPool2D(2, 2, 0),\n",
    "\n",
    "            Conv2D(32, 64, 3, 1, 1),\n",
    "            BatchNorm2D(64),\n",
    "            ReLU(),\n",
    "            MaxPool2D(2, 2, 0), \n",
    "\n",
    "            Conv2D(64, 128, 3, 1, 1),\n",
    "            BatchNorm2D(128),\n",
    "            ReLU(),\n",
    "            MaxPool2D(2, 2, 0), \n",
    "\n",
    "            Conv2D(128, 256, 3, 1, 1),\n",
    "            BatchNorm2D(256),\n",
    "            ReLU(),\n",
    "            MaxPool2D(2, 2, 0), \n",
    "\n",
    "            Conv2D(256, 512, 3, 1, 1),\n",
    "            BatchNorm2D(512),\n",
    "            ReLU(),\n",
    "            MaxPool2D(2, 2, 0), \n",
    "\n",
    "            Conv2D(512, 1024, 3, 1, 1),\n",
    "            BatchNorm2D(1024),\n",
    "            ReLU(),\n",
    "            MaxPool2D(2, 2, 0)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.conv(x).squeeze()\n",
    "        return x\n",
    "\n",
    "class LabelPredictor(nn.Layer):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(LabelPredictor, self).__init__()\n",
    "\n",
    "        self.layer = Sequential(\n",
    "            Linear(1024, 512),\n",
    "            ReLU(),\n",
    "\n",
    "            Linear(512, 256),\n",
    "            ReLU(),\n",
    "\n",
    "            Linear(256, 128),\n",
    "            ReLU(),\n",
    "\n",
    "            Linear(128, 7),\n",
    "        )\n",
    "\n",
    "    def forward(self, h):\n",
    "        c = self.layer(h)\n",
    "        return c\n",
    "\n",
    "\n",
    "class DomainClassifier(nn.Layer):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(DomainClassifier, self).__init__()\n",
    "\n",
    "        self.layer = Sequential(\n",
    "            Linear(1024, 512),\n",
    "            BatchNorm1D(512),\n",
    "            ReLU(),\n",
    "\n",
    "            Linear(512, 256),\n",
    "            BatchNorm1D(256),\n",
    "            ReLU(),\n",
    "\n",
    "            Linear(256, 128),\n",
    "            BatchNorm1D(128),\n",
    "            ReLU(),\n",
    "\n",
    "            Linear(128, 1),\n",
    "        )\n",
    "\n",
    "    def forward(self, h):\n",
    "        y = self.layer(h)\n",
    "        return y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre training\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_bsize = 128 #32\n",
    "eval_bsize = 128\n",
    "\n",
    "# 训练轮次可以增加\n",
    "nepoch = 400\n",
    "# TODO\n",
    "# Medium Baseline要求找一个较好的lamb，可以参照DaNN原文提及的将lamb设置成动态变化\n",
    "lamb = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# fix random seed\n",
    "def same_seeds(seed):\n",
    "    paddle.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "\n",
    "# fix feed for reproducibility\n",
    "same_seeds(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "source_dataloader = DataLoader(source_dataset, batch_size=train_bsize, shuffle=True)\n",
    "target_dataloader = DataLoader(target_dataset, batch_size=train_bsize, shuffle=True)\n",
    "valid_dataloader = DataLoader(valid_dataset, batch_size=eval_bsize, shuffle=False)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=eval_bsize, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "feature_extractor = FeatureExtractor()\n",
    "label_predictor = LabelPredictor()\n",
    "domain_classifier = DomainClassifier()\n",
    "\n",
    "class_criterion = paddle.nn.loss.CrossEntropyLoss()\n",
    "domain_criterion = paddle.nn.BCEWithLogitsLoss()\n",
    "\n",
    "optimizer_F = optim.Adam(parameters=feature_extractor.parameters())\n",
    "optimizer_C = optim.Adam(parameters=label_predictor.parameters())\n",
    "optimizer_D = optim.Adam(parameters=domain_classifier.parameters())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funtion\n",
    "\n",
    "* DaNN Implementation：\n",
    "\n",
    "    In the original paper, Gradient Reversal Layer is used.Feature Extractor, Label Predictor, and Domain Classifier are all trained at the same time. In this code, we train Domain Classifier first, and then train our Feature Extractor (same concept as Generator and Discriminator training process in GAN).\n",
    "\n",
    "* Reminder\n",
    "    * Lambda, which controls the domain adversarial loss, is adaptive in the original paper. You can refer to [the original work](https://arxiv.org/pdf/1505.07818.pdf) . Here lambda is set to 0.01."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train_source_only(source_dataloader):\n",
    "    '''\n",
    "      Args:\n",
    "        source_dataloader: source data的dataloader\n",
    "    '''\n",
    "    # F loss: Feature Extrator & Label Predictor的loss\n",
    "    running_F_loss = 0.0\n",
    "    F_hit, F_num = 0.0, 0.0\n",
    "    for i, (source_data, source_label) in enumerate(source_dataloader):\n",
    "\n",
    "        feature = feature_extractor(source_data)\n",
    "        class_logits = label_predictor(feature[:source_data.shape[0]])\n",
    "        # loss为class CE。\n",
    "        loss = class_criterion(class_logits, source_label)\n",
    "        running_F_loss+= loss.numpy()\n",
    "\n",
    "        loss.backward()\n",
    "        optimizer_F.step()\n",
    "        optimizer_C.step()\n",
    "\n",
    "        optimizer_F.clear_grad()\n",
    "        optimizer_C.clear_grad()\n",
    "        \n",
    "        F_hit += np.sum((paddle.argmax(class_logits, axis=1) == source_label).numpy())\n",
    "        F_num += source_data.shape[0]\n",
    "        print(i, end='\\r')\n",
    "\n",
    "    return running_F_loss / (i+1), F_hit / F_num\n",
    "\n",
    "def train_dann(source_dataloader, target_dataloader, lamb):\n",
    "    '''\n",
    "      Args:\n",
    "        source_dataloader: source data的dataloader\n",
    "        target_dataloader: target data的dataloader\n",
    "        lamb: 调控adversarial的loss系数。\n",
    "    '''\n",
    "    # D loss: Domain Classifier的loss\n",
    "    # F loss: Feature Extrator & Label Predictor的loss\n",
    "    running_D_loss, running_F_loss = 0.0, 0.0\n",
    "    F_hit, F_num = 0.0, 0.0\n",
    "    D_hit, D_num = 0.0, 0.0\n",
    "\n",
    "    for i, ((source_data, source_label), (target_data, _)) in enumerate(zip(source_dataloader, target_dataloader)):\n",
    "        \n",
    "        # 我们把source data和target data混在一起，否则batch_norm可能会算错 (两边的data的mean/var不太一样)\n",
    "        mixed_data = paddle.concat([source_data, target_data], axis=0)\n",
    "        domain_label = paddle.zeros([source_data.shape[0] + target_data.shape[0], 1])\n",
    "        # 设定source data的label为1\n",
    "        domain_label[:source_data.shape[0]] = 1\n",
    "\n",
    "        # Step 1 : 训练Domain Classifier\n",
    "        feature = feature_extractor(mixed_data)\n",
    "        # 因为我们在Step 1不需要训练Feature Extractor，所以把feature做detach阶段梯度避免loss反向传播后更新Feature Extractor的参数。\n",
    "        domain_logits = domain_classifier(feature.detach())\n",
    "        loss = domain_criterion(domain_logits, domain_label)\n",
    "        running_D_loss+= loss.numpy()\n",
    "        loss.backward()\n",
    "        optimizer_D.step()\n",
    "        \n",
    "        domain_pred = 1*(F.sigmoid(domain_logits).numpy()>0.5).astype('int64')\n",
    "        D_hit += np.sum(domain_pred == domain_label.numpy())\n",
    "        D_num += mixed_data.shape[0]\n",
    "\n",
    "\n",
    "        # Step 2 : 训练Feature Extractor和Label Predictor\n",
    "        class_logits = label_predictor(feature[:source_data.shape[0]])\n",
    "        domain_logits = domain_classifier(feature)\n",
    "        # loss为原本的class CE 减去 lamb * domain BCE。\n",
    "        loss = class_criterion(class_logits, source_label)\n",
    "        running_F_loss += loss.numpy()\n",
    "        loss -= lamb * domain_criterion(domain_logits, domain_label)\n",
    "\n",
    "        loss.backward()\n",
    "        optimizer_F.step()\n",
    "        optimizer_C.step()\n",
    "\n",
    "        optimizer_D.clear_grad()\n",
    "        optimizer_F.clear_grad()\n",
    "        optimizer_C.clear_grad()\n",
    "        \n",
    "        F_hit += np.sum((paddle.argmax(class_logits, axis=1) == source_label).numpy())\n",
    "        F_num += source_data.shape[0]\n",
    "        print(i, end='\\r')\n",
    "\n",
    "    return running_D_loss / (i+1), running_F_loss / (i+1), F_hit / F_num, D_hit / D_num\n",
    "\n",
    "def valid_epoch(valid_dataloader, acc_manager):\n",
    "    running_F_loss = 0.0\n",
    "\n",
    "    feature_extractor.eval()\n",
    "    label_predictor.eval()\n",
    "\n",
    "    # Iterate the validation set by batches.\n",
    "    for i, data in enumerate(valid_dataloader):\n",
    "\n",
    "        # A batch consists of image data and corresponding labels.\n",
    "        x_data, y_data = data\n",
    "        labels = paddle.unsqueeze(y_data, axis=1)\n",
    "        # We don't need gradient in validation.\n",
    "        # Using paddle.no_grad() accelerates the forward process.\n",
    "        with paddle.no_grad():\n",
    "            feat = feature_extractor(x_data)\n",
    "            logits = label_predictor(feat)\n",
    "\n",
    "            # We can still compute the loss (but not the gradient).\n",
    "            loss = class_criterion(logits, y_data)\n",
    "            running_F_loss += loss.numpy()\n",
    "\n",
    "            # Compute the accuracy for current batch.\n",
    "            correct = acc_manager.compute(logits, labels)\n",
    "            acc_manager.update(correct)\n",
    "\n",
    "    feature_extractor.train()\n",
    "    label_predictor.train()\n",
    "\n",
    "    return running_F_loss / (i+1)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Source Only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "save_path = 'work/model/SourceOnly'\n",
    "best_acc = 0.0\n",
    "valid_acc = 0.0\n",
    "loss_record = {'trainF': {'loss': [], 'iter': []}, 'valid': {'loss': [], 'iter': []}}   # for recording loss\n",
    "acc_record = {'trainF': {'acc': [], 'iter': []}, 'valid': {'acc': [], 'iter': []}}      # for recording accuracy\n",
    "it = 0\n",
    "# 训练\n",
    "for epoch in range(nepoch):\n",
    "    train_F_loss, trainF_acc = train_source_only(source_dataloader)\n",
    "\n",
    "    # log for train curve\n",
    "    loss_record['trainF']['loss'].append(train_F_loss)\n",
    "    loss_record['trainF']['iter'].append(it)\n",
    "    acc_record['trainF']['acc'].append(trainF_acc)\n",
    "    acc_record['trainF']['iter'].append(it)\n",
    "    print('epoch {:>3d}: train F loss: {:6.4f}, trainF acc {:6.4f}'.format(epoch, train_F_loss, trainF_acc))\n",
    "\n",
    "    valid_acc_manager = paddle.metric.Accuracy()\n",
    "    valid_F_loss = valid_epoch(valid_dataloader, valid_acc_manager)\n",
    "    valid_acc = valid_acc_manager.accumulate()\n",
    "    # log for valid curve\n",
    "    loss_record['valid']['loss'].append(valid_F_loss)\n",
    "    loss_record['valid']['iter'].append(it)\n",
    "    acc_record['valid']['acc'].append(valid_acc)\n",
    "    acc_record['valid']['iter'].append(it)\n",
    "    print('epoch {:>3d}: valid F loss: {:6.4f}, valid acc {:6.4f}'.format(epoch, valid_F_loss, valid_acc))\n",
    "\n",
    "    it += 1\n",
    "    \n",
    "    if valid_acc > best_acc:\n",
    "        best_acc = valid_acc\n",
    "        paddle.save(feature_extractor.state_dict(), os.path.join(save_path, 'extractor_model.pdparams'))\n",
    "        paddle.save(optimizer_F.state_dict(), os.path.join(save_path, 'extractor_optim.pdopt'))\n",
    "        paddle.save(label_predictor.state_dict(), os.path.join(save_path, 'predictor_model.pdparams'))\n",
    "        paddle.save(optimizer_C.state_dict(), os.path.join(save_path, 'predictor_optim.pdopt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train DaNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\anaconda3\\envs\\paddle_cuda\\lib\\site-packages\\paddle\\nn\\layer\\norm.py:818: UserWarning: When training, we now always track global mean and variance.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   0: train D loss: 0.1513, train F loss: 2.6252, trainF acc 0.2814, trainD acc 0.9544\n",
      "epoch   0: valid F loss: 2.6855, valid acc 0.1548\n",
      "epoch   1: train D loss: 0.3043, train F loss: 1.9577, trainF acc 0.3551, trainD acc 0.9200\n",
      "epoch   1: valid F loss: 2.9461, valid acc 0.1548\n",
      "epoch   2: train D loss: 0.1905, train F loss: 1.3930, trainF acc 0.4599, trainD acc 0.9558\n",
      "epoch   2: valid F loss: 3.1790, valid acc 0.1946\n",
      "epoch   3: train D loss: 0.1221, train F loss: 1.2636, trainF acc 0.5048, trainD acc 0.9731\n",
      "epoch   3: valid F loss: 3.0732, valid acc 0.2116\n",
      "epoch   4: train D loss: 0.1392, train F loss: 1.3121, trainF acc 0.5084, trainD acc 0.9743\n",
      "epoch   4: valid F loss: 2.5004, valid acc 0.2614\n",
      "epoch   5: train D loss: 0.1422, train F loss: 1.2040, trainF acc 0.5593, trainD acc 0.9676\n",
      "epoch   5: valid F loss: 2.4303, valid acc 0.2372\n",
      "epoch   6: train D loss: 0.1223, train F loss: 1.1806, trainF acc 0.5754, trainD acc 0.9700\n",
      "epoch   6: valid F loss: 2.4781, valid acc 0.2415\n",
      "epoch   7: train D loss: 0.3212, train F loss: 1.2374, trainF acc 0.5407, trainD acc 0.8940\n",
      "epoch   7: valid F loss: 3.2396, valid acc 0.1918\n",
      "epoch   8: train D loss: 0.2020, train F loss: 1.0678, trainF acc 0.5766, trainD acc 0.9443\n",
      "epoch   8: valid F loss: 2.0988, valid acc 0.3082\n",
      "epoch   9: train D loss: 0.1914, train F loss: 0.9906, trainF acc 0.6126, trainD acc 0.9463\n",
      "epoch   9: valid F loss: 1.9788, valid acc 0.3253\n",
      "epoch  10: train D loss: 0.1904, train F loss: 0.9350, trainF acc 0.6479, trainD acc 0.9419\n",
      "epoch  10: valid F loss: 2.3486, valid acc 0.2585\n",
      "epoch  11: train D loss: 0.2262, train F loss: 1.1009, trainF acc 0.6120, trainD acc 0.9391\n",
      "epoch  11: valid F loss: 2.2431, valid acc 0.3764\n",
      "epoch  12: train D loss: 0.1679, train F loss: 1.1851, trainF acc 0.5784, trainD acc 0.9477\n",
      "epoch  12: valid F loss: 2.1927, valid acc 0.3438\n",
      "epoch  13: train D loss: 0.3541, train F loss: 1.0509, trainF acc 0.6144, trainD acc 0.8651\n",
      "epoch  13: valid F loss: 1.8828, valid acc 0.3594\n",
      "epoch  14: train D loss: 0.2727, train F loss: 0.9267, trainF acc 0.6503, trainD acc 0.9058\n",
      "epoch  14: valid F loss: 2.2241, valid acc 0.2429\n",
      "epoch  15: train D loss: 0.2364, train F loss: 0.8318, trainF acc 0.6575, trainD acc 0.9119\n",
      "epoch  15: valid F loss: 2.6596, valid acc 0.1747\n",
      "epoch  16: train D loss: 0.2104, train F loss: 0.9887, trainF acc 0.6359, trainD acc 0.9284\n",
      "epoch  16: valid F loss: 2.1376, valid acc 0.3395\n",
      "epoch  17: train D loss: 0.1776, train F loss: 0.9325, trainF acc 0.6611, trainD acc 0.9445\n",
      "epoch  17: valid F loss: 2.1236, valid acc 0.3778\n",
      "epoch  18: train D loss: 0.1762, train F loss: 0.8822, trainF acc 0.6731, trainD acc 0.9393\n",
      "epoch  18: valid F loss: 2.4390, valid acc 0.3395\n",
      "epoch  19: train D loss: 0.2452, train F loss: 0.8204, trainF acc 0.6778, trainD acc 0.9151\n",
      "epoch  19: valid F loss: 2.2456, valid acc 0.3281\n",
      "epoch  20: train D loss: 0.2665, train F loss: 0.7887, trainF acc 0.7222, trainD acc 0.9006\n",
      "epoch  20: valid F loss: 2.3825, valid acc 0.3168\n",
      "epoch  21: train D loss: 0.2367, train F loss: 0.8383, trainF acc 0.6916, trainD acc 0.9102\n",
      "epoch  21: valid F loss: 2.2695, valid acc 0.3949\n",
      "epoch  22: train D loss: 0.2704, train F loss: 0.7086, trainF acc 0.7246, trainD acc 0.8986\n",
      "epoch  22: valid F loss: 1.9584, valid acc 0.4347\n",
      "epoch  23: train D loss: 0.2445, train F loss: 0.6653, trainF acc 0.7419, trainD acc 0.9105\n",
      "epoch  23: valid F loss: 2.0805, valid acc 0.4148\n",
      "epoch  24: train D loss: 0.2157, train F loss: 0.7436, trainF acc 0.7671, trainD acc 0.9261\n",
      "epoch  24: valid F loss: 2.0755, valid acc 0.4403\n",
      "epoch  25: train D loss: 0.2682, train F loss: 0.6852, trainF acc 0.7234, trainD acc 0.8966\n",
      "epoch  25: valid F loss: 1.8787, valid acc 0.4688\n",
      "epoch  26: train D loss: 0.2436, train F loss: 0.6345, trainF acc 0.7515, trainD acc 0.9168\n",
      "epoch  26: valid F loss: 1.9381, valid acc 0.3949\n",
      "epoch  27: train D loss: 0.2352, train F loss: 0.6214, trainF acc 0.7629, trainD acc 0.9203\n",
      "epoch  27: valid F loss: 2.3242, valid acc 0.3523\n",
      "epoch  28: train D loss: 0.2660, train F loss: 0.6221, trainF acc 0.7647, trainD acc 0.9006\n",
      "epoch  28: valid F loss: 2.3891, valid acc 0.4105\n",
      "epoch  29: train D loss: 0.2968, train F loss: 0.6850, trainF acc 0.7671, trainD acc 0.8798\n",
      "epoch  29: valid F loss: 2.6884, valid acc 0.3565\n",
      "epoch  30: train D loss: 0.5642, train F loss: 1.1453, trainF acc 0.6263, trainD acc 0.7536\n",
      "epoch  30: valid F loss: 2.5110, valid acc 0.1861\n",
      "epoch  31: train D loss: 0.4065, train F loss: 0.9094, trainF acc 0.6940, trainD acc 0.8270\n",
      "epoch  31: valid F loss: 2.3568, valid acc 0.3736\n",
      "epoch  32: train D loss: 0.3642, train F loss: 0.8099, trainF acc 0.6970, trainD acc 0.8640\n",
      "epoch  32: valid F loss: 2.1720, valid acc 0.3736\n",
      "epoch  33: train D loss: 0.2871, train F loss: 0.6953, trainF acc 0.7275, trainD acc 0.8980\n",
      "epoch  33: valid F loss: 2.2994, valid acc 0.4205\n",
      "epoch  34: train D loss: 0.2445, train F loss: 0.6617, trainF acc 0.7371, trainD acc 0.9139\n",
      "epoch  34: valid F loss: 2.9202, valid acc 0.3082\n",
      "epoch  35: train D loss: 0.2187, train F loss: 0.6200, trainF acc 0.7557, trainD acc 0.9229\n",
      "epoch  35: valid F loss: 2.3606, valid acc 0.3977\n",
      "epoch  36: train D loss: 0.2432, train F loss: 0.5480, trainF acc 0.7928, trainD acc 0.9107\n",
      "epoch  36: valid F loss: 1.8313, valid acc 0.4957\n",
      "epoch  37: train D loss: 0.2543, train F loss: 0.5326, trainF acc 0.8030, trainD acc 0.9093\n",
      "epoch  37: valid F loss: 1.9879, valid acc 0.4915\n",
      "epoch  38: train D loss: 0.2589, train F loss: 0.5543, trainF acc 0.8000, trainD acc 0.9079\n",
      "epoch  38: valid F loss: 2.7378, valid acc 0.4219\n",
      "epoch  39: train D loss: 0.2577, train F loss: 0.7683, trainF acc 0.7246, trainD acc 0.9061\n",
      "epoch  39: valid F loss: 1.9962, valid acc 0.4347\n",
      "epoch  40: train D loss: 0.3240, train F loss: 0.6482, trainF acc 0.7796, trainD acc 0.8836\n",
      "epoch  40: valid F loss: 2.2193, valid acc 0.3438\n",
      "epoch  41: train D loss: 0.3056, train F loss: 0.5773, trainF acc 0.7898, trainD acc 0.8810\n",
      "epoch  41: valid F loss: 2.1437, valid acc 0.4830\n",
      "epoch  42: train D loss: 0.2758, train F loss: 0.5703, trainF acc 0.7922, trainD acc 0.8888\n",
      "epoch  42: valid F loss: 1.9381, valid acc 0.4332\n",
      "epoch  43: train D loss: 0.2640, train F loss: 0.5090, trainF acc 0.7994, trainD acc 0.9053\n",
      "epoch  43: valid F loss: 2.0657, valid acc 0.4929\n",
      "epoch  44: train D loss: 0.2548, train F loss: 0.4303, trainF acc 0.8479, trainD acc 0.9081\n",
      "epoch  44: valid F loss: 1.9399, valid acc 0.5142\n",
      "epoch  45: train D loss: 0.2402, train F loss: 0.3742, trainF acc 0.8563, trainD acc 0.9110\n",
      "epoch  45: valid F loss: 2.1117, valid acc 0.4460\n",
      "epoch  46: train D loss: 0.2304, train F loss: 0.4080, trainF acc 0.8635, trainD acc 0.9220\n",
      "epoch  46: valid F loss: 2.6350, valid acc 0.4006\n",
      "epoch  47: train D loss: 0.2156, train F loss: 0.4080, trainF acc 0.8641, trainD acc 0.9278\n",
      "epoch  47: valid F loss: 2.5290, valid acc 0.4048\n",
      "epoch  48: train D loss: 0.2533, train F loss: 0.5606, trainF acc 0.7994, trainD acc 0.9012\n",
      "epoch  48: valid F loss: 2.2930, valid acc 0.4744\n",
      "epoch  49: train D loss: 0.2588, train F loss: 0.4795, trainF acc 0.8317, trainD acc 0.8995\n",
      "epoch  49: valid F loss: 2.0191, valid acc 0.4688\n",
      "epoch  50: train D loss: 0.2463, train F loss: 0.3626, trainF acc 0.8665, trainD acc 0.9061\n",
      "epoch  50: valid F loss: 2.0128, valid acc 0.5284\n",
      "epoch  51: train D loss: 0.2683, train F loss: 0.3180, trainF acc 0.8898, trainD acc 0.8966\n",
      "epoch  51: valid F loss: 1.9967, valid acc 0.5028\n",
      "epoch  52: train D loss: 0.2577, train F loss: 0.3772, trainF acc 0.8647, trainD acc 0.9001\n",
      "epoch  52: valid F loss: 2.0694, valid acc 0.5270\n",
      "epoch  53: train D loss: 0.2378, train F loss: 0.3711, trainF acc 0.8671, trainD acc 0.9151\n",
      "epoch  53: valid F loss: 2.0371, valid acc 0.5440\n",
      "epoch  54: train D loss: 0.2714, train F loss: 0.4180, trainF acc 0.8485, trainD acc 0.8957\n",
      "epoch  54: valid F loss: 2.2247, valid acc 0.4801\n",
      "epoch  55: train D loss: 0.2904, train F loss: 0.3847, trainF acc 0.8587, trainD acc 0.8905\n",
      "epoch  55: valid F loss: 2.8472, valid acc 0.4119\n",
      "epoch  56: train D loss: 0.3353, train F loss: 0.4111, trainF acc 0.8754, trainD acc 0.8657\n",
      "epoch  56: valid F loss: 2.4940, valid acc 0.4588\n",
      "epoch  57: train D loss: 0.2653, train F loss: 0.3252, trainF acc 0.8713, trainD acc 0.9047\n",
      "epoch  57: valid F loss: 2.3264, valid acc 0.4545\n",
      "epoch  58: train D loss: 0.2069, train F loss: 0.2778, trainF acc 0.8946, trainD acc 0.9315\n",
      "epoch  58: valid F loss: 2.3686, valid acc 0.5185\n",
      "epoch  59: train D loss: 0.2183, train F loss: 0.2821, trainF acc 0.8886, trainD acc 0.9229\n",
      "epoch  59: valid F loss: 2.6777, valid acc 0.4872\n",
      "epoch  60: train D loss: 0.3301, train F loss: 0.3411, trainF acc 0.8874, trainD acc 0.8663\n",
      "epoch  60: valid F loss: 2.6036, valid acc 0.4375\n",
      "epoch  61: train D loss: 0.3525, train F loss: 0.3801, trainF acc 0.8665, trainD acc 0.8512\n",
      "epoch  61: valid F loss: 2.2309, valid acc 0.4673\n",
      "epoch  62: train D loss: 0.2949, train F loss: 0.5424, trainF acc 0.8186, trainD acc 0.8836\n",
      "epoch  62: valid F loss: 3.0795, valid acc 0.3281\n",
      "epoch  63: train D loss: 0.2607, train F loss: 0.5094, trainF acc 0.8174, trainD acc 0.8975\n",
      "epoch  63: valid F loss: 2.5996, valid acc 0.3949\n",
      "epoch  64: train D loss: 0.2764, train F loss: 0.4179, trainF acc 0.8455, trainD acc 0.8865\n",
      "epoch  64: valid F loss: 3.0262, valid acc 0.4134\n",
      "epoch  65: train D loss: 0.3131, train F loss: 0.4439, trainF acc 0.8377, trainD acc 0.8697\n",
      "epoch  65: valid F loss: 2.4740, valid acc 0.4034\n",
      "epoch  66: train D loss: 0.2693, train F loss: 0.6615, trainF acc 0.7796, trainD acc 0.9021\n",
      "epoch  66: valid F loss: 3.2812, valid acc 0.2543\n",
      "epoch  67: train D loss: 0.2950, train F loss: 0.5195, trainF acc 0.8054, trainD acc 0.8833\n",
      "epoch  67: valid F loss: 2.3650, valid acc 0.4474\n",
      "epoch  68: train D loss: 0.2876, train F loss: 0.3688, trainF acc 0.8641, trainD acc 0.8810\n",
      "epoch  68: valid F loss: 2.6364, valid acc 0.4730\n",
      "epoch  69: train D loss: 0.2772, train F loss: 0.3860, trainF acc 0.8689, trainD acc 0.8885\n",
      "epoch  69: valid F loss: 2.7095, valid acc 0.4020\n",
      "epoch  70: train D loss: 0.2840, train F loss: 0.3242, trainF acc 0.8796, trainD acc 0.8859\n",
      "epoch  70: valid F loss: 2.5624, valid acc 0.4517\n",
      "epoch  71: train D loss: 0.2995, train F loss: 0.2839, trainF acc 0.8910, trainD acc 0.8764\n",
      "epoch  71: valid F loss: 2.4390, valid acc 0.4744\n",
      "epoch  72: train D loss: 0.2572, train F loss: 0.3034, trainF acc 0.9006, trainD acc 0.8989\n",
      "epoch  72: valid F loss: 2.4756, valid acc 0.4517\n",
      "epoch  73: train D loss: 0.2252, train F loss: 0.3956, trainF acc 0.8653, trainD acc 0.9168\n",
      "epoch  73: valid F loss: 2.6221, valid acc 0.4545\n",
      "epoch  74: train D loss: 0.2590, train F loss: 0.4302, trainF acc 0.8347, trainD acc 0.9029\n",
      "epoch  74: valid F loss: 2.0687, valid acc 0.5241\n",
      "epoch  75: train D loss: 0.3208, train F loss: 0.3625, trainF acc 0.8635, trainD acc 0.8616\n",
      "epoch  75: valid F loss: 2.6068, valid acc 0.4134\n",
      "epoch  76: train D loss: 0.2841, train F loss: 0.2691, trainF acc 0.8982, trainD acc 0.8816\n",
      "epoch  76: valid F loss: 2.6410, valid acc 0.4759\n",
      "epoch  77: train D loss: 0.2707, train F loss: 0.2272, trainF acc 0.9240, trainD acc 0.8891\n",
      "epoch  77: valid F loss: 2.9666, valid acc 0.3281\n",
      "epoch  78: train D loss: 0.2387, train F loss: 0.3726, trainF acc 0.8868, trainD acc 0.9079\n",
      "epoch  78: valid F loss: 2.3528, valid acc 0.4744\n",
      "epoch  79: train D loss: 0.2335, train F loss: 0.2827, trainF acc 0.8970, trainD acc 0.9093\n",
      "epoch  79: valid F loss: 3.0214, valid acc 0.4332\n",
      "epoch  80: train D loss: 0.3327, train F loss: 0.2208, trainF acc 0.9341, trainD acc 0.8631\n",
      "epoch  80: valid F loss: 2.5398, valid acc 0.5369\n",
      "epoch  81: train D loss: 0.3038, train F loss: 0.3638, trainF acc 0.8790, trainD acc 0.8700\n",
      "epoch  81: valid F loss: 2.6415, valid acc 0.5000\n",
      "epoch  82: train D loss: 0.2446, train F loss: 0.2832, trainF acc 0.8970, trainD acc 0.9032\n",
      "epoch  82: valid F loss: 3.0079, valid acc 0.4588\n",
      "epoch  83: train D loss: 0.2661, train F loss: 0.3781, trainF acc 0.8946, trainD acc 0.8899\n",
      "epoch  83: valid F loss: 3.5989, valid acc 0.3693\n",
      "epoch  84: train D loss: 0.3238, train F loss: 0.4183, trainF acc 0.8503, trainD acc 0.8527\n",
      "epoch  84: valid F loss: 2.2639, valid acc 0.4801\n",
      "epoch  85: train D loss: 0.3216, train F loss: 0.3219, trainF acc 0.8808, trainD acc 0.8605\n",
      "epoch  85: valid F loss: 2.3907, valid acc 0.4702\n",
      "epoch  86: train D loss: 0.2776, train F loss: 0.2846, trainF acc 0.9072, trainD acc 0.8850\n",
      "epoch  86: valid F loss: 3.0273, valid acc 0.4744\n",
      "epoch  87: train D loss: 0.2794, train F loss: 0.4222, trainF acc 0.8413, trainD acc 0.8905\n",
      "epoch  87: valid F loss: 2.5954, valid acc 0.4006\n",
      "epoch  88: train D loss: 0.2846, train F loss: 0.3971, trainF acc 0.8850, trainD acc 0.8847\n",
      "epoch  88: valid F loss: 2.0472, valid acc 0.5270\n",
      "epoch  89: train D loss: 0.2566, train F loss: 0.3375, trainF acc 0.8749, trainD acc 0.9055\n",
      "epoch  89: valid F loss: 2.2068, valid acc 0.4261\n",
      "epoch  90: train D loss: 0.2161, train F loss: 0.2040, trainF acc 0.9335, trainD acc 0.9174\n",
      "epoch  90: valid F loss: 2.2632, valid acc 0.5426\n",
      "epoch  91: train D loss: 0.2730, train F loss: 0.1357, trainF acc 0.9515, trainD acc 0.8914\n",
      "epoch  91: valid F loss: 2.7715, valid acc 0.4773\n",
      "epoch  92: train D loss: 0.3411, train F loss: 0.1447, trainF acc 0.9581, trainD acc 0.8460\n",
      "epoch  92: valid F loss: 2.5935, valid acc 0.5341\n",
      "epoch  93: train D loss: 0.3675, train F loss: 0.3627, trainF acc 0.8910, trainD acc 0.8322\n",
      "epoch  93: valid F loss: 2.7650, valid acc 0.4247\n",
      "epoch  94: train D loss: 0.3775, train F loss: 0.2834, trainF acc 0.9000, trainD acc 0.8385\n",
      "epoch  94: valid F loss: 3.4956, valid acc 0.4162\n",
      "epoch  95: train D loss: 0.3087, train F loss: 0.2124, trainF acc 0.9222, trainD acc 0.8697\n",
      "epoch  95: valid F loss: 2.8309, valid acc 0.4631\n",
      "epoch  96: train D loss: 0.3211, train F loss: 0.1520, trainF acc 0.9419, trainD acc 0.8703\n",
      "epoch  96: valid F loss: 2.3451, valid acc 0.5028\n",
      "epoch  97: train D loss: 0.3203, train F loss: 0.1305, trainF acc 0.9521, trainD acc 0.8628\n",
      "epoch  97: valid F loss: 3.4020, valid acc 0.4489\n",
      "epoch  98: train D loss: 0.3175, train F loss: 0.1215, trainF acc 0.9521, trainD acc 0.8674\n",
      "epoch  98: valid F loss: 2.6222, valid acc 0.5114\n",
      "epoch  99: train D loss: 0.3267, train F loss: 0.1071, trainF acc 0.9569, trainD acc 0.8527\n",
      "epoch  99: valid F loss: 2.8116, valid acc 0.4943\n",
      "epoch 100: train D loss: 0.3390, train F loss: 0.1097, trainF acc 0.9539, trainD acc 0.8576\n",
      "epoch 100: valid F loss: 3.0896, valid acc 0.4943\n",
      "epoch 101: train D loss: 0.3339, train F loss: 0.0973, trainF acc 0.9707, trainD acc 0.8533\n",
      "epoch 101: valid F loss: 3.5361, valid acc 0.5298\n",
      "epoch 102: train D loss: 0.3263, train F loss: 0.0894, trainF acc 0.9659, trainD acc 0.8550\n",
      "epoch 102: valid F loss: 3.2220, valid acc 0.4915\n",
      "epoch 103: train D loss: 0.3299, train F loss: 0.0935, trainF acc 0.9766, trainD acc 0.8654\n",
      "epoch 103: valid F loss: 3.5815, valid acc 0.4460\n",
      "epoch 104: train D loss: 0.3691, train F loss: 0.3412, trainF acc 0.9138, trainD acc 0.8328\n",
      "epoch 104: valid F loss: 5.9931, valid acc 0.2727\n",
      "epoch 105: train D loss: 0.4269, train F loss: 0.2912, trainF acc 0.9150, trainD acc 0.8001\n",
      "epoch 105: valid F loss: 2.3609, valid acc 0.4645\n",
      "epoch 106: train D loss: 0.3719, train F loss: 0.3327, trainF acc 0.8856, trainD acc 0.8440\n",
      "epoch 106: valid F loss: 4.3441, valid acc 0.4276\n",
      "epoch 107: train D loss: 0.3119, train F loss: 0.3343, trainF acc 0.8778, trainD acc 0.8700\n",
      "epoch 107: valid F loss: 2.9774, valid acc 0.5128\n",
      "epoch 108: train D loss: 0.4268, train F loss: 0.3862, trainF acc 0.8635, trainD acc 0.8065\n",
      "epoch 108: valid F loss: 4.1294, valid acc 0.3665\n",
      "epoch 109: train D loss: 0.4077, train F loss: 0.2800, trainF acc 0.8964, trainD acc 0.8140\n",
      "epoch 109: valid F loss: 2.4095, valid acc 0.4929\n",
      "epoch 110: train D loss: 0.3606, train F loss: 0.2994, trainF acc 0.8862, trainD acc 0.8443\n",
      "epoch 110: valid F loss: 3.7889, valid acc 0.4830\n",
      "epoch 111: train D loss: 0.3395, train F loss: 0.1925, trainF acc 0.9293, trainD acc 0.8556\n",
      "epoch 111: valid F loss: 2.4963, valid acc 0.5256\n",
      "epoch 112: train D loss: 0.3475, train F loss: 0.1590, trainF acc 0.9539, trainD acc 0.8420\n",
      "epoch 112: valid F loss: 2.7825, valid acc 0.5213\n",
      "epoch 113: train D loss: 0.3632, train F loss: 0.4437, trainF acc 0.8862, trainD acc 0.8385\n",
      "epoch 113: valid F loss: 2.7915, valid acc 0.4332\n",
      "epoch 114: train D loss: 0.3890, train F loss: 0.3734, trainF acc 0.8820, trainD acc 0.8235\n",
      "epoch 114: valid F loss: 3.3898, valid acc 0.3509\n",
      "epoch 115: train D loss: 0.3523, train F loss: 0.4497, trainF acc 0.8479, trainD acc 0.8588\n",
      "epoch 115: valid F loss: 2.1560, valid acc 0.4531\n",
      "epoch 116: train D loss: 0.3791, train F loss: 0.2849, trainF acc 0.9090, trainD acc 0.8380\n",
      "epoch 116: valid F loss: 2.4653, valid acc 0.4688\n",
      "epoch 117: train D loss: 0.3938, train F loss: 0.2659, trainF acc 0.9066, trainD acc 0.8281\n",
      "epoch 117: valid F loss: 3.2741, valid acc 0.4830\n",
      "epoch 118: train D loss: 0.3219, train F loss: 0.2313, trainF acc 0.9263, trainD acc 0.8657\n",
      "epoch 118: valid F loss: 2.8022, valid acc 0.4716\n",
      "epoch 119: train D loss: 0.2858, train F loss: 0.2346, trainF acc 0.9120, trainD acc 0.8836\n",
      "epoch 119: valid F loss: 2.6681, valid acc 0.4645\n",
      "epoch 120: train D loss: 0.3453, train F loss: 0.1545, trainF acc 0.9431, trainD acc 0.8504\n",
      "epoch 120: valid F loss: 2.9969, valid acc 0.4702\n",
      "epoch 121: train D loss: 0.3776, train F loss: 0.1078, trainF acc 0.9629, trainD acc 0.8325\n",
      "epoch 121: valid F loss: 2.9107, valid acc 0.4830\n",
      "epoch 122: train D loss: 0.2858, train F loss: 0.0884, trainF acc 0.9701, trainD acc 0.8761\n",
      "epoch 122: valid F loss: 3.2004, valid acc 0.4673\n",
      "epoch 123: train D loss: 0.2991, train F loss: 0.0657, trainF acc 0.9754, trainD acc 0.8723\n",
      "epoch 123: valid F loss: 4.4574, valid acc 0.4702\n",
      "epoch 124: train D loss: 0.3404, train F loss: 0.0771, trainF acc 0.9754, trainD acc 0.8420\n",
      "epoch 124: valid F loss: 3.3268, valid acc 0.5057\n",
      "epoch 125: train D loss: 0.3406, train F loss: 0.0938, trainF acc 0.9647, trainD acc 0.8440\n",
      "epoch 125: valid F loss: 4.2476, valid acc 0.4815\n",
      "epoch 126: train D loss: 0.3020, train F loss: 0.0976, trainF acc 0.9659, trainD acc 0.8729\n",
      "epoch 126: valid F loss: 3.4030, valid acc 0.5227\n",
      "epoch 127: train D loss: 0.3324, train F loss: 0.1067, trainF acc 0.9653, trainD acc 0.8518\n",
      "epoch 127: valid F loss: 4.1280, valid acc 0.4759\n",
      "epoch 128: train D loss: 0.3812, train F loss: 0.1463, trainF acc 0.9557, trainD acc 0.8290\n",
      "epoch 128: valid F loss: 4.8141, valid acc 0.4517\n",
      "epoch 129: train D loss: 0.3754, train F loss: 0.1261, trainF acc 0.9599, trainD acc 0.8290\n",
      "epoch 129: valid F loss: 3.7267, valid acc 0.4901\n",
      "epoch 130: train D loss: 0.3747, train F loss: 0.1265, trainF acc 0.9593, trainD acc 0.8264\n",
      "epoch 130: valid F loss: 3.7552, valid acc 0.4858\n",
      "epoch 131: train D loss: 0.3316, train F loss: 0.2226, trainF acc 0.9240, trainD acc 0.8498\n",
      "epoch 131: valid F loss: 3.3367, valid acc 0.5014\n",
      "epoch 132: train D loss: 0.3384, train F loss: 0.1334, trainF acc 0.9527, trainD acc 0.8492\n",
      "epoch 132: valid F loss: 3.1058, valid acc 0.4986\n",
      "epoch 133: train D loss: 0.3853, train F loss: 0.1542, trainF acc 0.9491, trainD acc 0.8218\n",
      "epoch 133: valid F loss: 3.5358, valid acc 0.5298\n",
      "epoch 134: train D loss: 0.3967, train F loss: 0.1126, trainF acc 0.9623, trainD acc 0.8096\n",
      "epoch 134: valid F loss: 3.2950, valid acc 0.5327\n",
      "epoch 135: train D loss: 0.3640, train F loss: 0.0730, trainF acc 0.9743, trainD acc 0.8313\n",
      "epoch 135: valid F loss: 3.4008, valid acc 0.4830\n",
      "epoch 136: train D loss: 0.3597, train F loss: 0.0830, trainF acc 0.9725, trainD acc 0.8229\n",
      "epoch 136: valid F loss: 3.0922, valid acc 0.5369\n",
      "epoch 137: train D loss: 0.3547, train F loss: 0.0530, trainF acc 0.9838, trainD acc 0.8330\n",
      "epoch 137: valid F loss: 3.4533, valid acc 0.4830\n",
      "epoch 138: train D loss: 0.3446, train F loss: 0.0481, trainF acc 0.9856, trainD acc 0.8449\n",
      "epoch 138: valid F loss: 3.7866, valid acc 0.4886\n",
      "epoch 139: train D loss: 0.3786, train F loss: 0.0567, trainF acc 0.9790, trainD acc 0.8140\n",
      "epoch 139: valid F loss: 3.8203, valid acc 0.5256\n",
      "epoch 140: train D loss: 0.3798, train F loss: 0.0604, trainF acc 0.9784, trainD acc 0.8261\n",
      "epoch 140: valid F loss: 3.4470, valid acc 0.5114\n",
      "epoch 141: train D loss: 0.3775, train F loss: 0.0630, trainF acc 0.9772, trainD acc 0.8290\n",
      "epoch 141: valid F loss: 3.8221, valid acc 0.5085\n",
      "epoch 142: train D loss: 0.3977, train F loss: 0.0604, trainF acc 0.9802, trainD acc 0.8030\n",
      "epoch 142: valid F loss: 3.7085, valid acc 0.5270\n",
      "epoch 143: train D loss: 0.4056, train F loss: 0.0835, trainF acc 0.9754, trainD acc 0.8192\n",
      "epoch 143: valid F loss: 3.8906, valid acc 0.5142\n",
      "epoch 144: train D loss: 0.3750, train F loss: 0.0991, trainF acc 0.9683, trainD acc 0.8261\n",
      "epoch 144: valid F loss: 3.6434, valid acc 0.4943\n",
      "epoch 145: train D loss: 0.4524, train F loss: 0.1041, trainF acc 0.9611, trainD acc 0.7900\n",
      "epoch 145: valid F loss: 3.0958, valid acc 0.4901\n",
      "epoch 146: train D loss: 0.3873, train F loss: 0.0707, trainF acc 0.9754, trainD acc 0.8166\n",
      "epoch 146: valid F loss: 3.3978, valid acc 0.4915\n",
      "epoch 147: train D loss: 0.4156, train F loss: 0.0785, trainF acc 0.9701, trainD acc 0.8166\n",
      "epoch 147: valid F loss: 3.7873, valid acc 0.5014\n",
      "epoch 148: train D loss: 0.4255, train F loss: 0.0916, trainF acc 0.9820, trainD acc 0.7938\n",
      "epoch 148: valid F loss: 4.2735, valid acc 0.4702\n",
      "epoch 149: train D loss: 0.4406, train F loss: 0.1888, trainF acc 0.9407, trainD acc 0.7657\n",
      "epoch 149: valid F loss: 3.8603, valid acc 0.4190\n",
      "epoch 150: train D loss: 0.3764, train F loss: 0.2326, trainF acc 0.9305, trainD acc 0.8307\n",
      "epoch 150: valid F loss: 3.5257, valid acc 0.4787\n",
      "epoch 151: train D loss: 0.3740, train F loss: 0.3121, trainF acc 0.9090, trainD acc 0.8374\n",
      "epoch 151: valid F loss: 3.6392, valid acc 0.3977\n",
      "epoch 152: train D loss: 0.4309, train F loss: 0.2715, trainF acc 0.9072, trainD acc 0.8018\n",
      "epoch 152: valid F loss: 2.6014, valid acc 0.4886\n",
      "epoch 153: train D loss: 0.3851, train F loss: 0.1558, trainF acc 0.9389, trainD acc 0.8267\n",
      "epoch 153: valid F loss: 2.5066, valid acc 0.4986\n",
      "epoch 154: train D loss: 0.4093, train F loss: 0.1006, trainF acc 0.9665, trainD acc 0.8024\n",
      "epoch 154: valid F loss: 3.0908, valid acc 0.5014\n",
      "epoch 155: train D loss: 0.3630, train F loss: 0.0909, trainF acc 0.9677, trainD acc 0.8267\n",
      "epoch 155: valid F loss: 2.8222, valid acc 0.5227\n",
      "epoch 156: train D loss: 0.3955, train F loss: 0.0591, trainF acc 0.9790, trainD acc 0.8042\n",
      "epoch 156: valid F loss: 2.9700, valid acc 0.5398\n",
      "epoch 157: train D loss: 0.4014, train F loss: 0.0484, trainF acc 0.9838, trainD acc 0.8036\n",
      "epoch 157: valid F loss: 3.2679, valid acc 0.5213\n",
      "epoch 158: train D loss: 0.3691, train F loss: 0.0399, trainF acc 0.9862, trainD acc 0.8293\n",
      "epoch 158: valid F loss: 3.5381, valid acc 0.5213\n",
      "epoch 159: train D loss: 0.3887, train F loss: 0.0439, trainF acc 0.9838, trainD acc 0.8146\n",
      "epoch 159: valid F loss: 3.2745, valid acc 0.5597\n",
      "epoch 160: train D loss: 0.3983, train F loss: 0.0402, trainF acc 0.9868, trainD acc 0.8010\n",
      "epoch 160: valid F loss: 3.9863, valid acc 0.4688\n",
      "epoch 161: train D loss: 0.4185, train F loss: 0.0611, trainF acc 0.9796, trainD acc 0.8039\n",
      "epoch 161: valid F loss: 3.4157, valid acc 0.5142\n",
      "epoch 162: train D loss: 0.4194, train F loss: 0.0520, trainF acc 0.9802, trainD acc 0.8117\n",
      "epoch 162: valid F loss: 3.4777, valid acc 0.5099\n",
      "epoch 163: train D loss: 0.3945, train F loss: 0.0411, trainF acc 0.9868, trainD acc 0.8180\n",
      "epoch 163: valid F loss: 3.6442, valid acc 0.5355\n",
      "epoch 164: train D loss: 0.4154, train F loss: 0.0976, trainF acc 0.9838, trainD acc 0.8195\n",
      "epoch 164: valid F loss: 3.9819, valid acc 0.5185\n",
      "epoch 165: train D loss: 0.4416, train F loss: 0.2719, trainF acc 0.9156, trainD acc 0.8044\n",
      "epoch 165: valid F loss: 3.5708, valid acc 0.4460\n",
      "epoch 166: train D loss: 0.4641, train F loss: 0.3063, trainF acc 0.9012, trainD acc 0.7750\n",
      "epoch 166: valid F loss: 3.5210, valid acc 0.3963\n",
      "epoch 167: train D loss: 0.3982, train F loss: 0.2027, trainF acc 0.9281, trainD acc 0.8102\n",
      "epoch 167: valid F loss: 2.8710, valid acc 0.4389\n",
      "epoch 168: train D loss: 0.3924, train F loss: 0.1272, trainF acc 0.9485, trainD acc 0.8200\n",
      "epoch 168: valid F loss: 3.3723, valid acc 0.4901\n",
      "epoch 169: train D loss: 0.3967, train F loss: 0.0871, trainF acc 0.9665, trainD acc 0.8146\n",
      "epoch 169: valid F loss: 2.8247, valid acc 0.5227\n",
      "epoch 170: train D loss: 0.3935, train F loss: 0.0646, trainF acc 0.9802, trainD acc 0.8108\n",
      "epoch 170: valid F loss: 3.1017, valid acc 0.5028\n",
      "epoch 171: train D loss: 0.3931, train F loss: 0.0475, trainF acc 0.9856, trainD acc 0.8088\n",
      "epoch 171: valid F loss: 3.1129, valid acc 0.5312\n",
      "epoch 172: train D loss: 0.3808, train F loss: 0.0478, trainF acc 0.9826, trainD acc 0.8200\n",
      "epoch 172: valid F loss: 3.7021, valid acc 0.5028\n",
      "epoch 173: train D loss: 0.3881, train F loss: 0.0489, trainF acc 0.9832, trainD acc 0.8224\n",
      "epoch 173: valid F loss: 3.5367, valid acc 0.5199\n",
      "epoch 174: train D loss: 0.4504, train F loss: 0.0479, trainF acc 0.9826, trainD acc 0.7822\n",
      "epoch 174: valid F loss: 3.2665, valid acc 0.5085\n",
      "epoch 175: train D loss: 0.3759, train F loss: 0.0392, trainF acc 0.9862, trainD acc 0.8394\n",
      "epoch 175: valid F loss: 3.7024, valid acc 0.5028\n",
      "epoch 176: train D loss: 0.3828, train F loss: 0.0401, trainF acc 0.9838, trainD acc 0.8166\n",
      "epoch 176: valid F loss: 3.9294, valid acc 0.4886\n",
      "epoch 177: train D loss: 0.4242, train F loss: 0.0590, trainF acc 0.9874, trainD acc 0.7909\n",
      "epoch 177: valid F loss: 3.7357, valid acc 0.5028\n",
      "epoch 178: train D loss: 0.4232, train F loss: 0.1584, trainF acc 0.9461, trainD acc 0.7865\n",
      "epoch 178: valid F loss: 5.1863, valid acc 0.3736\n",
      "epoch 179: train D loss: 0.3869, train F loss: 0.1140, trainF acc 0.9575, trainD acc 0.8137\n",
      "epoch 179: valid F loss: 3.6963, valid acc 0.5185\n",
      "epoch 180: train D loss: 0.3999, train F loss: 0.0791, trainF acc 0.9719, trainD acc 0.8068\n",
      "epoch 180: valid F loss: 3.2688, valid acc 0.5256\n",
      "epoch 181: train D loss: 0.4200, train F loss: 0.0563, trainF acc 0.9850, trainD acc 0.7984\n",
      "epoch 181: valid F loss: 3.1461, valid acc 0.5241\n",
      "epoch 182: train D loss: 0.4095, train F loss: 0.1131, trainF acc 0.9635, trainD acc 0.7972\n",
      "epoch 182: valid F loss: 4.0651, valid acc 0.5028\n",
      "epoch 183: train D loss: 0.3942, train F loss: 0.0788, trainF acc 0.9695, trainD acc 0.8122\n",
      "epoch 183: valid F loss: 3.8933, valid acc 0.4929\n",
      "epoch 184: train D loss: 0.4362, train F loss: 0.0464, trainF acc 0.9826, trainD acc 0.7819\n",
      "epoch 184: valid F loss: 2.6864, valid acc 0.5213\n",
      "epoch 185: train D loss: 0.4132, train F loss: 0.1270, trainF acc 0.9796, trainD acc 0.8018\n",
      "epoch 185: valid F loss: 4.0931, valid acc 0.5156\n",
      "epoch 186: train D loss: 0.4347, train F loss: 0.2213, trainF acc 0.9347, trainD acc 0.7785\n",
      "epoch 186: valid F loss: 3.7358, valid acc 0.4787\n",
      "epoch 187: train D loss: 0.4278, train F loss: 0.1872, trainF acc 0.9335, trainD acc 0.8062\n",
      "epoch 187: valid F loss: 3.1753, valid acc 0.4503\n",
      "epoch 188: train D loss: 0.4172, train F loss: 0.1054, trainF acc 0.9629, trainD acc 0.7969\n",
      "epoch 188: valid F loss: 3.0942, valid acc 0.4972\n",
      "epoch 189: train D loss: 0.4619, train F loss: 0.0679, trainF acc 0.9737, trainD acc 0.7643\n",
      "epoch 189: valid F loss: 2.8405, valid acc 0.4773\n",
      "epoch 190: train D loss: 0.4296, train F loss: 0.0437, trainF acc 0.9874, trainD acc 0.7837\n",
      "epoch 190: valid F loss: 3.5686, valid acc 0.5028\n",
      "epoch 191: train D loss: 0.3775, train F loss: 0.0397, trainF acc 0.9898, trainD acc 0.8255\n",
      "epoch 191: valid F loss: 4.0347, valid acc 0.4901\n",
      "epoch 192: train D loss: 0.3971, train F loss: 0.0500, trainF acc 0.9820, trainD acc 0.8068\n",
      "epoch 192: valid F loss: 4.5976, valid acc 0.4673\n",
      "epoch 193: train D loss: 0.4601, train F loss: 0.0579, trainF acc 0.9784, trainD acc 0.7565\n",
      "epoch 193: valid F loss: 4.1330, valid acc 0.5156\n",
      "epoch 194: train D loss: 0.4402, train F loss: 0.0946, trainF acc 0.9707, trainD acc 0.8010\n",
      "epoch 194: valid F loss: 3.6971, valid acc 0.4957\n",
      "epoch 195: train D loss: 0.3806, train F loss: 0.0566, trainF acc 0.9826, trainD acc 0.8313\n",
      "epoch 195: valid F loss: 4.5932, valid acc 0.5043\n",
      "epoch 196: train D loss: 0.4434, train F loss: 0.0614, trainF acc 0.9772, trainD acc 0.7860\n",
      "epoch 196: valid F loss: 3.7266, valid acc 0.5241\n",
      "epoch 197: train D loss: 0.4708, train F loss: 0.0430, trainF acc 0.9850, trainD acc 0.7681\n",
      "epoch 197: valid F loss: 3.7306, valid acc 0.5142\n",
      "epoch 198: train D loss: 0.4327, train F loss: 0.0361, trainF acc 0.9868, trainD acc 0.7906\n",
      "epoch 198: valid F loss: 3.9747, valid acc 0.5057\n",
      "epoch 199: train D loss: 0.4215, train F loss: 0.0373, trainF acc 0.9904, trainD acc 0.8065\n",
      "epoch 199: valid F loss: 3.6979, valid acc 0.5298\n",
      "epoch 200: train D loss: 0.4201, train F loss: 0.0375, trainF acc 0.9862, trainD acc 0.7880\n",
      "epoch 200: valid F loss: 3.2353, valid acc 0.5156\n",
      "epoch 201: train D loss: 0.4372, train F loss: 0.0484, trainF acc 0.9838, trainD acc 0.7776\n",
      "epoch 201: valid F loss: 3.8500, valid acc 0.5412\n",
      "epoch 202: train D loss: 0.4327, train F loss: 0.0441, trainF acc 0.9826, trainD acc 0.7900\n",
      "epoch 202: valid F loss: 4.0660, valid acc 0.5284\n",
      "epoch 203: train D loss: 0.4382, train F loss: 0.0318, trainF acc 0.9892, trainD acc 0.7785\n",
      "epoch 203: valid F loss: 4.1517, valid acc 0.5426\n",
      "epoch 204: train D loss: 0.4834, train F loss: 0.0309, trainF acc 0.9862, trainD acc 0.7556\n",
      "epoch 204: valid F loss: 3.5780, valid acc 0.5071\n",
      "epoch 205: train D loss: 0.4263, train F loss: 0.0245, trainF acc 0.9898, trainD acc 0.7932\n",
      "epoch 205: valid F loss: 5.0297, valid acc 0.4347\n",
      "epoch 206: train D loss: 0.4879, train F loss: 0.0364, trainF acc 0.9856, trainD acc 0.7730\n",
      "epoch 206: valid F loss: 5.6405, valid acc 0.4787\n",
      "epoch 207: train D loss: 0.4615, train F loss: 0.0408, trainF acc 0.9874, trainD acc 0.7863\n",
      "epoch 207: valid F loss: 3.8216, valid acc 0.5142\n",
      "epoch 208: train D loss: 0.4850, train F loss: 0.0380, trainF acc 0.9874, trainD acc 0.7525\n",
      "epoch 208: valid F loss: 4.9674, valid acc 0.5071\n",
      "epoch 209: train D loss: 0.4988, train F loss: 0.1472, trainF acc 0.9647, trainD acc 0.7637\n",
      "epoch 209: valid F loss: 4.0072, valid acc 0.4716\n",
      "epoch 210: train D loss: 0.5123, train F loss: 0.2952, trainF acc 0.9132, trainD acc 0.7374\n",
      "epoch 210: valid F loss: 2.8297, valid acc 0.4574\n",
      "epoch 211: train D loss: 0.4692, train F loss: 0.1508, trainF acc 0.9569, trainD acc 0.7701\n",
      "epoch 211: valid F loss: 3.2557, valid acc 0.5043\n",
      "epoch 212: train D loss: 0.5266, train F loss: 0.1847, trainF acc 0.9407, trainD acc 0.7149\n",
      "epoch 212: valid F loss: 3.0297, valid acc 0.4602\n",
      "epoch 213: train D loss: 0.5052, train F loss: 0.1183, trainF acc 0.9551, trainD acc 0.7371\n",
      "epoch 213: valid F loss: 3.4527, valid acc 0.4844\n",
      "epoch 214: train D loss: 0.4438, train F loss: 0.1077, trainF acc 0.9695, trainD acc 0.7802\n",
      "epoch 214: valid F loss: 3.7892, valid acc 0.4801\n",
      "epoch 215: train D loss: 0.4592, train F loss: 0.2274, trainF acc 0.9347, trainD acc 0.7715\n",
      "epoch 215: valid F loss: 3.1953, valid acc 0.4091\n",
      "epoch 216: train D loss: 0.5252, train F loss: 0.1200, trainF acc 0.9653, trainD acc 0.7259\n",
      "epoch 216: valid F loss: 2.6045, valid acc 0.5312\n",
      "epoch 217: train D loss: 0.5092, train F loss: 0.0538, trainF acc 0.9796, trainD acc 0.7343\n",
      "epoch 217: valid F loss: 3.4886, valid acc 0.4716\n",
      "epoch 218: train D loss: 0.4303, train F loss: 0.0471, trainF acc 0.9844, trainD acc 0.8076\n",
      "epoch 218: valid F loss: 3.4892, valid acc 0.5043\n",
      "epoch 219: train D loss: 0.4229, train F loss: 0.0278, trainF acc 0.9898, trainD acc 0.8050\n",
      "epoch 219: valid F loss: 4.0290, valid acc 0.4901\n",
      "epoch 220: train D loss: 0.4970, train F loss: 0.0321, trainF acc 0.9886, trainD acc 0.7504\n",
      "epoch 220: valid F loss: 3.9949, valid acc 0.5312\n",
      "epoch 221: train D loss: 0.5193, train F loss: 0.0546, trainF acc 0.9808, trainD acc 0.7276\n",
      "epoch 221: valid F loss: 3.4462, valid acc 0.4915\n",
      "epoch 222: train D loss: 0.4908, train F loss: 0.0411, trainF acc 0.9850, trainD acc 0.7481\n",
      "epoch 222: valid F loss: 3.2954, valid acc 0.5142\n",
      "epoch 223: train D loss: 0.4766, train F loss: 0.0503, trainF acc 0.9832, trainD acc 0.7724\n",
      "epoch 223: valid F loss: 3.8618, valid acc 0.4844\n",
      "epoch 224: train D loss: 0.4994, train F loss: 0.0359, trainF acc 0.9874, trainD acc 0.7441\n",
      "epoch 224: valid F loss: 3.5860, valid acc 0.5142\n",
      "epoch 225: train D loss: 0.4834, train F loss: 0.0263, trainF acc 0.9916, trainD acc 0.7637\n",
      "epoch 225: valid F loss: 3.7150, valid acc 0.5284\n",
      "epoch 226: train D loss: 0.4508, train F loss: 0.0789, trainF acc 0.9886, trainD acc 0.7842\n",
      "epoch 226: valid F loss: 4.5926, valid acc 0.5312\n",
      "epoch 227: train D loss: 0.5174, train F loss: 0.3431, trainF acc 0.9240, trainD acc 0.7366\n",
      "epoch 227: valid F loss: 3.2544, valid acc 0.5014\n",
      "epoch 228: train D loss: 0.5116, train F loss: 0.2704, trainF acc 0.9150, trainD acc 0.7331\n",
      "epoch 228: valid F loss: 2.5356, valid acc 0.4389\n",
      "epoch 229: train D loss: 0.4754, train F loss: 0.2562, trainF acc 0.9168, trainD acc 0.7663\n",
      "epoch 229: valid F loss: 3.6681, valid acc 0.4830\n",
      "epoch 230: train D loss: 0.4870, train F loss: 0.2124, trainF acc 0.9305, trainD acc 0.7597\n",
      "epoch 230: valid F loss: 3.9402, valid acc 0.4901\n",
      "epoch 231: train D loss: 0.4867, train F loss: 0.1554, trainF acc 0.9533, trainD acc 0.7753\n",
      "epoch 231: valid F loss: 4.3753, valid acc 0.4645\n",
      "epoch 232: train D loss: 0.4966, train F loss: 0.0897, trainF acc 0.9725, trainD acc 0.7649\n",
      "epoch 232: valid F loss: 3.9730, valid acc 0.5043\n",
      "epoch 233: train D loss: 0.5032, train F loss: 0.0959, trainF acc 0.9671, trainD acc 0.7369\n",
      "epoch 233: valid F loss: 3.7040, valid acc 0.4801\n",
      "epoch 234: train D loss: 0.5020, train F loss: 0.0843, trainF acc 0.9689, trainD acc 0.7455\n",
      "epoch 234: valid F loss: 5.0905, valid acc 0.4389\n",
      "epoch 235: train D loss: 0.4756, train F loss: 0.0527, trainF acc 0.9826, trainD acc 0.7629\n",
      "epoch 235: valid F loss: 4.8935, valid acc 0.4134\n",
      "epoch 236: train D loss: 0.5006, train F loss: 0.0525, trainF acc 0.9814, trainD acc 0.7354\n",
      "epoch 236: valid F loss: 4.3500, valid acc 0.4560\n",
      "epoch 237: train D loss: 0.4421, train F loss: 0.0293, trainF acc 0.9886, trainD acc 0.7860\n",
      "epoch 237: valid F loss: 4.2033, valid acc 0.4915\n",
      "epoch 238: train D loss: 0.4419, train F loss: 0.0375, trainF acc 0.9850, trainD acc 0.7839\n",
      "epoch 238: valid F loss: 5.1109, valid acc 0.4645\n",
      "epoch 239: train D loss: 0.5205, train F loss: 0.0361, trainF acc 0.9862, trainD acc 0.7259\n",
      "epoch 239: valid F loss: 4.9546, valid acc 0.4460\n",
      "epoch 240: train D loss: 0.4942, train F loss: 0.0292, trainF acc 0.9904, trainD acc 0.7389\n",
      "epoch 240: valid F loss: 5.3969, valid acc 0.4489\n",
      "epoch 241: train D loss: 0.4948, train F loss: 0.0304, trainF acc 0.9880, trainD acc 0.7481\n",
      "epoch 241: valid F loss: 5.7973, valid acc 0.4290\n",
      "epoch 242: train D loss: 0.4588, train F loss: 0.0370, trainF acc 0.9862, trainD acc 0.7675\n",
      "epoch 242: valid F loss: 4.7285, valid acc 0.4943\n",
      "epoch 243: train D loss: 0.4922, train F loss: 0.0331, trainF acc 0.9904, trainD acc 0.7519\n",
      "epoch 243: valid F loss: 4.8249, valid acc 0.5213\n",
      "epoch 244: train D loss: 0.4929, train F loss: 0.0372, trainF acc 0.9868, trainD acc 0.7360\n",
      "epoch 244: valid F loss: 5.0548, valid acc 0.4787\n",
      "epoch 245: train D loss: 0.4709, train F loss: 0.0332, trainF acc 0.9874, trainD acc 0.7626\n",
      "epoch 245: valid F loss: 5.1577, valid acc 0.4773\n",
      "epoch 246: train D loss: 0.4845, train F loss: 0.0330, trainF acc 0.9862, trainD acc 0.7464\n",
      "epoch 246: valid F loss: 4.6225, valid acc 0.5156\n",
      "epoch 247: train D loss: 0.4874, train F loss: 0.0331, trainF acc 0.9886, trainD acc 0.7623\n",
      "epoch 247: valid F loss: 4.7703, valid acc 0.5398\n",
      "epoch 248: train D loss: 0.4965, train F loss: 0.0948, trainF acc 0.9892, trainD acc 0.7516\n",
      "epoch 248: valid F loss: 4.6033, valid acc 0.5284\n",
      "epoch 249: train D loss: 0.5463, train F loss: 0.2133, trainF acc 0.9437, trainD acc 0.7091\n",
      "epoch 249: valid F loss: 4.8390, valid acc 0.4560\n",
      "epoch 250: train D loss: 0.4533, train F loss: 0.1052, trainF acc 0.9641, trainD acc 0.7773\n",
      "epoch 250: valid F loss: 4.0444, valid acc 0.4787\n",
      "epoch 251: train D loss: 0.4580, train F loss: 0.0814, trainF acc 0.9814, trainD acc 0.7681\n",
      "epoch 251: valid F loss: 4.2432, valid acc 0.4972\n",
      "epoch 252: train D loss: 0.5646, train F loss: 0.3480, trainF acc 0.9060, trainD acc 0.6849\n",
      "epoch 252: valid F loss: 2.3026, valid acc 0.5028\n",
      "epoch 253: train D loss: 0.5703, train F loss: 0.1767, trainF acc 0.9401, trainD acc 0.6892\n",
      "epoch 253: valid F loss: 2.6041, valid acc 0.4886\n",
      "epoch 254: train D loss: 0.4963, train F loss: 0.1449, trainF acc 0.9689, trainD acc 0.7418\n",
      "epoch 254: valid F loss: 3.0452, valid acc 0.5384\n",
      "epoch 255: train D loss: 0.5325, train F loss: 0.4680, trainF acc 0.8916, trainD acc 0.7423\n",
      "epoch 255: valid F loss: 2.3993, valid acc 0.4560\n",
      "epoch 256: train D loss: 0.5990, train F loss: 0.3447, trainF acc 0.8749, trainD acc 0.6698\n",
      "epoch 256: valid F loss: 3.6415, valid acc 0.4006\n",
      "epoch 257: train D loss: 0.5281, train F loss: 0.1530, trainF acc 0.9485, trainD acc 0.7213\n",
      "epoch 257: valid F loss: 3.3235, valid acc 0.4389\n",
      "epoch 258: train D loss: 0.5350, train F loss: 0.0877, trainF acc 0.9683, trainD acc 0.7140\n",
      "epoch 258: valid F loss: 3.7210, valid acc 0.4773\n",
      "epoch 259: train D loss: 0.5055, train F loss: 0.0557, trainF acc 0.9802, trainD acc 0.7400\n",
      "epoch 259: valid F loss: 3.2506, valid acc 0.5170\n",
      "epoch 260: train D loss: 0.5157, train F loss: 0.0485, trainF acc 0.9814, trainD acc 0.7499\n",
      "epoch 260: valid F loss: 3.5562, valid acc 0.4886\n",
      "epoch 261: train D loss: 0.4886, train F loss: 0.0532, trainF acc 0.9784, trainD acc 0.7432\n",
      "epoch 261: valid F loss: 3.3657, valid acc 0.5185\n",
      "epoch 262: train D loss: 0.5208, train F loss: 0.0814, trainF acc 0.9844, trainD acc 0.7279\n",
      "epoch 262: valid F loss: 3.8299, valid acc 0.4702\n",
      "epoch 263: train D loss: 0.5234, train F loss: 0.1332, trainF acc 0.9509, trainD acc 0.7111\n",
      "epoch 263: valid F loss: 4.5950, valid acc 0.4134\n",
      "epoch 264: train D loss: 0.4852, train F loss: 0.0933, trainF acc 0.9671, trainD acc 0.7455\n",
      "epoch 264: valid F loss: 3.6099, valid acc 0.4759\n",
      "epoch 265: train D loss: 0.4791, train F loss: 0.1047, trainF acc 0.9790, trainD acc 0.7536\n",
      "epoch 265: valid F loss: 3.7172, valid acc 0.4972\n",
      "epoch 266: train D loss: 0.4637, train F loss: 0.0874, trainF acc 0.9641, trainD acc 0.7597\n",
      "epoch 266: valid F loss: 4.0315, valid acc 0.4702\n",
      "epoch 267: train D loss: 0.4792, train F loss: 0.0822, trainF acc 0.9760, trainD acc 0.7539\n",
      "epoch 267: valid F loss: 2.5734, valid acc 0.5483\n",
      "epoch 268: train D loss: 0.4923, train F loss: 0.0424, trainF acc 0.9862, trainD acc 0.7556\n",
      "epoch 268: valid F loss: 3.2748, valid acc 0.5227\n",
      "epoch 269: train D loss: 0.4677, train F loss: 0.0416, trainF acc 0.9844, trainD acc 0.7776\n",
      "epoch 269: valid F loss: 3.7120, valid acc 0.5085\n",
      "epoch 270: train D loss: 0.4857, train F loss: 0.0354, trainF acc 0.9862, trainD acc 0.7545\n",
      "epoch 270: valid F loss: 3.8083, valid acc 0.5028\n",
      "epoch 271: train D loss: 0.5911, train F loss: 0.0312, trainF acc 0.9892, trainD acc 0.6782\n",
      "epoch 271: valid F loss: 4.1072, valid acc 0.4986\n",
      "epoch 272: train D loss: 0.5012, train F loss: 0.0527, trainF acc 0.9910, trainD acc 0.7369\n",
      "epoch 272: valid F loss: 4.2652, valid acc 0.5000\n",
      "epoch 273: train D loss: 0.4260, train F loss: 0.1917, trainF acc 0.9425, trainD acc 0.7987\n",
      "epoch 273: valid F loss: 3.7068, valid acc 0.4901\n",
      "epoch 274: train D loss: 0.5117, train F loss: 0.1070, trainF acc 0.9599, trainD acc 0.7369\n",
      "epoch 274: valid F loss: 3.3995, valid acc 0.4901\n",
      "epoch 275: train D loss: 0.5318, train F loss: 0.1590, trainF acc 0.9521, trainD acc 0.7325\n",
      "epoch 275: valid F loss: 2.8860, valid acc 0.5298\n",
      "epoch 276: train D loss: 0.5099, train F loss: 0.1588, trainF acc 0.9629, trainD acc 0.7331\n",
      "epoch 276: valid F loss: 3.1027, valid acc 0.5028\n",
      "epoch 277: train D loss: 0.5309, train F loss: 0.2518, trainF acc 0.9251, trainD acc 0.7241\n",
      "epoch 277: valid F loss: 3.0512, valid acc 0.4418\n",
      "epoch 278: train D loss: 0.4899, train F loss: 0.2146, trainF acc 0.9419, trainD acc 0.7551\n",
      "epoch 278: valid F loss: 3.2726, valid acc 0.4759\n",
      "epoch 279: train D loss: 0.4944, train F loss: 0.2364, trainF acc 0.9090, trainD acc 0.7542\n",
      "epoch 279: valid F loss: 3.3747, valid acc 0.4446\n",
      "epoch 280: train D loss: 0.5048, train F loss: 0.1447, trainF acc 0.9605, trainD acc 0.7345\n",
      "epoch 280: valid F loss: 3.4016, valid acc 0.4901\n",
      "epoch 281: train D loss: 0.5152, train F loss: 0.0979, trainF acc 0.9683, trainD acc 0.7395\n",
      "epoch 281: valid F loss: 4.0669, valid acc 0.4673\n",
      "epoch 282: train D loss: 0.5028, train F loss: 0.1026, trainF acc 0.9671, trainD acc 0.7343\n",
      "epoch 282: valid F loss: 3.9828, valid acc 0.4517\n",
      "epoch 283: train D loss: 0.4740, train F loss: 0.1272, trainF acc 0.9569, trainD acc 0.7499\n",
      "epoch 283: valid F loss: 3.1193, valid acc 0.5384\n",
      "epoch 284: train D loss: 0.4550, train F loss: 0.0770, trainF acc 0.9778, trainD acc 0.7863\n",
      "epoch 284: valid F loss: 2.9032, valid acc 0.5057\n",
      "epoch 285: train D loss: 0.5121, train F loss: 0.0507, trainF acc 0.9844, trainD acc 0.7311\n",
      "epoch 285: valid F loss: 3.1556, valid acc 0.4929\n",
      "epoch 286: train D loss: 0.5285, train F loss: 0.0439, trainF acc 0.9862, trainD acc 0.7236\n",
      "epoch 286: valid F loss: 3.2259, valid acc 0.5227\n",
      "epoch 287: train D loss: 0.4871, train F loss: 0.0261, trainF acc 0.9910, trainD acc 0.7579\n",
      "epoch 287: valid F loss: 3.8394, valid acc 0.4844\n",
      "epoch 288: train D loss: 0.4815, train F loss: 0.0238, trainF acc 0.9952, trainD acc 0.7715\n",
      "epoch 288: valid F loss: 4.4858, valid acc 0.5071\n",
      "epoch 289: train D loss: 0.5180, train F loss: 0.0231, trainF acc 0.9922, trainD acc 0.7357\n",
      "epoch 289: valid F loss: 4.7386, valid acc 0.5170\n",
      "epoch 290: train D loss: 0.5436, train F loss: 0.0178, trainF acc 0.9934, trainD acc 0.7146\n",
      "epoch 290: valid F loss: 4.7181, valid acc 0.5142\n",
      "epoch 291: train D loss: 0.5390, train F loss: 0.0273, trainF acc 0.9934, trainD acc 0.7204\n",
      "epoch 291: valid F loss: 4.6408, valid acc 0.5369\n",
      "epoch 292: train D loss: 0.4856, train F loss: 0.0225, trainF acc 0.9898, trainD acc 0.7686\n",
      "epoch 292: valid F loss: 4.4732, valid acc 0.5327\n",
      "epoch 293: train D loss: 0.5058, train F loss: 0.0329, trainF acc 0.9862, trainD acc 0.7490\n",
      "epoch 293: valid F loss: 4.3986, valid acc 0.4815\n",
      "epoch 294: train D loss: 0.4877, train F loss: 0.0422, trainF acc 0.9844, trainD acc 0.7692\n",
      "epoch 294: valid F loss: 4.2944, valid acc 0.5270\n",
      "epoch 295: train D loss: 0.5062, train F loss: 0.0171, trainF acc 0.9940, trainD acc 0.7441\n",
      "epoch 295: valid F loss: 4.5591, valid acc 0.5170\n",
      "epoch 296: train D loss: 0.5291, train F loss: 0.0184, trainF acc 0.9940, trainD acc 0.7262\n",
      "epoch 296: valid F loss: 4.4825, valid acc 0.5241\n",
      "epoch 297: train D loss: 0.4726, train F loss: 0.0268, trainF acc 0.9892, trainD acc 0.7831\n",
      "epoch 297: valid F loss: 4.6119, valid acc 0.5384\n",
      "epoch 298: train D loss: 0.4555, train F loss: 0.0337, trainF acc 0.9916, trainD acc 0.7912\n",
      "epoch 298: valid F loss: 4.1523, valid acc 0.5355\n",
      "epoch 299: train D loss: 0.5793, train F loss: 0.0896, trainF acc 0.9814, trainD acc 0.7114\n",
      "epoch 299: valid F loss: 5.6769, valid acc 0.4730\n",
      "epoch 300: train D loss: 0.5315, train F loss: 0.1435, trainF acc 0.9545, trainD acc 0.7270\n",
      "epoch 300: valid F loss: 3.6391, valid acc 0.4830\n",
      "epoch 301: train D loss: 0.4916, train F loss: 0.0615, trainF acc 0.9784, trainD acc 0.7591\n",
      "epoch 301: valid F loss: 3.8412, valid acc 0.5028\n",
      "epoch 302: train D loss: 0.6015, train F loss: 0.0332, trainF acc 0.9880, trainD acc 0.6840\n",
      "epoch 302: valid F loss: 4.0060, valid acc 0.5071\n",
      "epoch 303: train D loss: 0.5454, train F loss: 0.0464, trainF acc 0.9904, trainD acc 0.7244\n",
      "epoch 303: valid F loss: 4.2233, valid acc 0.5341\n",
      "epoch 304: train D loss: 0.5411, train F loss: 0.0658, trainF acc 0.9760, trainD acc 0.7270\n",
      "epoch 304: valid F loss: 4.4960, valid acc 0.5185\n",
      "epoch 305: train D loss: 0.5604, train F loss: 0.0523, trainF acc 0.9832, trainD acc 0.7155\n",
      "epoch 305: valid F loss: 3.7782, valid acc 0.5384\n",
      "epoch 306: train D loss: 0.5323, train F loss: 0.0547, trainF acc 0.9808, trainD acc 0.7317\n",
      "epoch 306: valid F loss: 4.2574, valid acc 0.4844\n",
      "epoch 307: train D loss: 0.5256, train F loss: 0.0396, trainF acc 0.9844, trainD acc 0.7302\n",
      "epoch 307: valid F loss: 4.0308, valid acc 0.4872\n",
      "epoch 308: train D loss: 0.5715, train F loss: 0.0365, trainF acc 0.9880, trainD acc 0.7005\n",
      "epoch 308: valid F loss: 4.1912, valid acc 0.5099\n",
      "epoch 309: train D loss: 0.5393, train F loss: 0.0214, trainF acc 0.9940, trainD acc 0.7204\n",
      "epoch 309: valid F loss: 4.4716, valid acc 0.5213\n",
      "epoch 310: train D loss: 0.5406, train F loss: 0.0197, trainF acc 0.9952, trainD acc 0.7279\n",
      "epoch 310: valid F loss: 4.4297, valid acc 0.5057\n",
      "epoch 311: train D loss: 0.5889, train F loss: 0.0416, trainF acc 0.9844, trainD acc 0.6739\n",
      "epoch 311: valid F loss: 4.1789, valid acc 0.5355\n",
      "epoch 312: train D loss: 0.5272, train F loss: 0.0285, trainF acc 0.9916, trainD acc 0.7337\n",
      "epoch 312: valid F loss: 4.2198, valid acc 0.5355\n",
      "epoch 313: train D loss: 0.5039, train F loss: 0.0170, trainF acc 0.9940, trainD acc 0.7406\n",
      "epoch 313: valid F loss: 4.2563, valid acc 0.5241\n",
      "epoch 314: train D loss: 0.5915, train F loss: 0.0169, trainF acc 0.9958, trainD acc 0.6820\n",
      "epoch 314: valid F loss: 4.6414, valid acc 0.5298\n",
      "epoch 315: train D loss: 0.5766, train F loss: 0.0339, trainF acc 0.9862, trainD acc 0.6837\n",
      "epoch 315: valid F loss: 4.8634, valid acc 0.5355\n",
      "epoch 316: train D loss: 0.5247, train F loss: 0.0194, trainF acc 0.9898, trainD acc 0.7421\n",
      "epoch 316: valid F loss: 4.5729, valid acc 0.5369\n",
      "epoch 317: train D loss: 0.6180, train F loss: 0.0409, trainF acc 0.9892, trainD acc 0.6623\n",
      "epoch 317: valid F loss: 4.0256, valid acc 0.5483\n",
      "epoch 318: train D loss: 0.5227, train F loss: 0.0389, trainF acc 0.9862, trainD acc 0.7314\n",
      "epoch 318: valid F loss: 4.4950, valid acc 0.5170\n",
      "epoch 319: train D loss: 0.5895, train F loss: 0.0240, trainF acc 0.9934, trainD acc 0.6886\n",
      "epoch 319: valid F loss: 4.3305, valid acc 0.4929\n",
      "epoch 320: train D loss: 0.6115, train F loss: 0.0308, trainF acc 0.9880, trainD acc 0.6655\n",
      "epoch 320: valid F loss: 4.2226, valid acc 0.5043\n",
      "epoch 321: train D loss: 0.5654, train F loss: 0.0091, trainF acc 0.9976, trainD acc 0.6979\n",
      "epoch 321: valid F loss: 4.4039, valid acc 0.5099\n",
      "epoch 322: train D loss: 0.5359, train F loss: 0.0199, trainF acc 0.9916, trainD acc 0.7230\n",
      "epoch 322: valid F loss: 4.5239, valid acc 0.5071\n",
      "epoch 323: train D loss: 0.5614, train F loss: 0.0107, trainF acc 0.9970, trainD acc 0.6840\n",
      "epoch 323: valid F loss: 4.5656, valid acc 0.4858\n",
      "epoch 324: train D loss: 0.5707, train F loss: 0.0184, trainF acc 0.9922, trainD acc 0.6976\n",
      "epoch 324: valid F loss: 4.5118, valid acc 0.5270\n",
      "epoch 325: train D loss: 0.5770, train F loss: 0.0170, trainF acc 0.9940, trainD acc 0.7201\n",
      "epoch 325: valid F loss: 5.1410, valid acc 0.5213\n",
      "epoch 326: train D loss: 0.6940, train F loss: 0.0230, trainF acc 0.9916, trainD acc 0.6173\n",
      "epoch 326: valid F loss: 5.1284, valid acc 0.5270\n",
      "epoch 327: train D loss: 0.6435, train F loss: 0.0466, trainF acc 0.9886, trainD acc 0.6430\n",
      "epoch 327: valid F loss: 4.7108, valid acc 0.4986\n",
      "epoch 328: train D loss: 0.4867, train F loss: 0.1541, trainF acc 0.9563, trainD acc 0.7825\n",
      "epoch 328: valid F loss: 4.8852, valid acc 0.4673\n",
      "epoch 329: train D loss: 0.5622, train F loss: 0.1414, trainF acc 0.9563, trainD acc 0.7005\n",
      "epoch 329: valid F loss: 4.2860, valid acc 0.5142\n",
      "epoch 330: train D loss: 0.6068, train F loss: 0.4059, trainF acc 0.9132, trainD acc 0.6574\n",
      "epoch 330: valid F loss: 2.9934, valid acc 0.4972\n",
      "epoch 331: train D loss: 0.5798, train F loss: 0.2943, trainF acc 0.9114, trainD acc 0.6947\n",
      "epoch 331: valid F loss: 2.6094, valid acc 0.4716\n",
      "epoch 332: train D loss: 0.5655, train F loss: 0.1802, trainF acc 0.9401, trainD acc 0.7057\n",
      "epoch 332: valid F loss: 2.4010, valid acc 0.5554\n",
      "epoch 333: train D loss: 0.5976, train F loss: 0.1206, trainF acc 0.9611, trainD acc 0.6961\n",
      "epoch 333: valid F loss: 2.6305, valid acc 0.5256\n",
      "epoch 334: train D loss: 0.5745, train F loss: 0.0801, trainF acc 0.9701, trainD acc 0.7022\n",
      "epoch 334: valid F loss: 3.6799, valid acc 0.4688\n",
      "epoch 335: train D loss: 0.6055, train F loss: 0.0533, trainF acc 0.9796, trainD acc 0.6762\n",
      "epoch 335: valid F loss: 4.6462, valid acc 0.4830\n",
      "epoch 336: train D loss: 0.6052, train F loss: 0.0507, trainF acc 0.9832, trainD acc 0.6753\n",
      "epoch 336: valid F loss: 4.5217, valid acc 0.4901\n",
      "epoch 337: train D loss: 0.6348, train F loss: 0.0327, trainF acc 0.9910, trainD acc 0.6334\n",
      "epoch 337: valid F loss: 4.1634, valid acc 0.5156\n",
      "epoch 338: train D loss: 0.5728, train F loss: 0.0290, trainF acc 0.9910, trainD acc 0.6898\n",
      "epoch 338: valid F loss: 4.0184, valid acc 0.5469\n",
      "epoch 339: train D loss: 0.6303, train F loss: 0.0342, trainF acc 0.9856, trainD acc 0.6470\n",
      "epoch 339: valid F loss: 3.9110, valid acc 0.5582\n",
      "epoch 340: train D loss: 0.5983, train F loss: 0.0332, trainF acc 0.9916, trainD acc 0.6802\n",
      "epoch 340: valid F loss: 4.1397, valid acc 0.5440\n",
      "epoch 341: train D loss: 0.6512, train F loss: 0.0672, trainF acc 0.9784, trainD acc 0.6265\n",
      "epoch 341: valid F loss: 4.1157, valid acc 0.4815\n",
      "epoch 342: train D loss: 0.5608, train F loss: 0.0465, trainF acc 0.9814, trainD acc 0.7042\n",
      "epoch 342: valid F loss: 3.9700, valid acc 0.5327\n",
      "epoch 343: train D loss: 0.6011, train F loss: 0.0551, trainF acc 0.9892, trainD acc 0.6762\n",
      "epoch 343: valid F loss: 4.5372, valid acc 0.5213\n",
      "epoch 344: train D loss: 0.6666, train F loss: 0.0529, trainF acc 0.9796, trainD acc 0.6519\n",
      "epoch 344: valid F loss: 4.5394, valid acc 0.5284\n",
      "epoch 345: train D loss: 0.6011, train F loss: 0.0779, trainF acc 0.9832, trainD acc 0.7010\n",
      "epoch 345: valid F loss: 3.5009, valid acc 0.5426\n",
      "epoch 346: train D loss: 0.5879, train F loss: 0.1241, trainF acc 0.9569, trainD acc 0.6834\n",
      "epoch 346: valid F loss: 3.6958, valid acc 0.5213\n",
      "epoch 347: train D loss: 0.6637, train F loss: 0.0575, trainF acc 0.9784, trainD acc 0.6127\n",
      "epoch 347: valid F loss: 3.2872, valid acc 0.5440\n",
      "epoch 348: train D loss: 0.6299, train F loss: 0.0544, trainF acc 0.9826, trainD acc 0.6476\n",
      "epoch 348: valid F loss: 3.5341, valid acc 0.5412\n",
      "epoch 349: train D loss: 0.5617, train F loss: 0.0277, trainF acc 0.9916, trainD acc 0.7169\n",
      "epoch 349: valid F loss: 3.6093, valid acc 0.5185\n",
      "epoch 350: train D loss: 0.5473, train F loss: 0.0142, trainF acc 0.9964, trainD acc 0.7296\n",
      "epoch 350: valid F loss: 3.7905, valid acc 0.5398\n",
      "epoch 351: train D loss: 0.6046, train F loss: 0.0144, trainF acc 0.9934, trainD acc 0.6745\n",
      "epoch 351: valid F loss: 4.7188, valid acc 0.5128\n",
      "epoch 352: train D loss: 0.6500, train F loss: 0.0410, trainF acc 0.9928, trainD acc 0.6436\n",
      "epoch 352: valid F loss: 4.9369, valid acc 0.5398\n",
      "epoch 353: train D loss: 0.6517, train F loss: 0.1170, trainF acc 0.9659, trainD acc 0.6179\n",
      "epoch 353: valid F loss: 4.7640, valid acc 0.4943\n",
      "epoch 354: train D loss: 0.5739, train F loss: 0.1243, trainF acc 0.9605, trainD acc 0.6852\n",
      "epoch 354: valid F loss: 5.9999, valid acc 0.4020\n",
      "epoch 355: train D loss: 0.5900, train F loss: 0.1000, trainF acc 0.9719, trainD acc 0.6623\n",
      "epoch 355: valid F loss: 3.8911, valid acc 0.4815\n",
      "epoch 356: train D loss: 0.5840, train F loss: 0.0673, trainF acc 0.9784, trainD acc 0.6878\n",
      "epoch 356: valid F loss: 3.9858, valid acc 0.5355\n",
      "epoch 357: train D loss: 0.6575, train F loss: 0.0605, trainF acc 0.9784, trainD acc 0.6277\n",
      "epoch 357: valid F loss: 3.5662, valid acc 0.5455\n",
      "epoch 358: train D loss: 0.6285, train F loss: 0.0286, trainF acc 0.9910, trainD acc 0.6557\n",
      "epoch 358: valid F loss: 3.4629, valid acc 0.5455\n",
      "epoch 359: train D loss: 0.5780, train F loss: 0.0327, trainF acc 0.9880, trainD acc 0.6982\n",
      "epoch 359: valid F loss: 3.8007, valid acc 0.5369\n",
      "epoch 360: train D loss: 0.5795, train F loss: 0.0348, trainF acc 0.9880, trainD acc 0.7036\n",
      "epoch 360: valid F loss: 4.3730, valid acc 0.5554\n",
      "epoch 361: train D loss: 0.6275, train F loss: 0.0269, trainF acc 0.9904, trainD acc 0.6485\n",
      "epoch 361: valid F loss: 4.3029, valid acc 0.5412\n",
      "epoch 362: train D loss: 0.6500, train F loss: 0.0258, trainF acc 0.9916, trainD acc 0.6268\n",
      "epoch 362: valid F loss: 4.4424, valid acc 0.5284\n",
      "epoch 363: train D loss: 0.6676, train F loss: 0.0233, trainF acc 0.9928, trainD acc 0.5962\n",
      "epoch 363: valid F loss: 4.6249, valid acc 0.5298\n",
      "epoch 364: train D loss: 0.6071, train F loss: 0.0338, trainF acc 0.9886, trainD acc 0.6626\n",
      "epoch 364: valid F loss: 4.9072, valid acc 0.5028\n",
      "epoch 365: train D loss: 0.5708, train F loss: 0.0309, trainF acc 0.9874, trainD acc 0.7008\n",
      "epoch 365: valid F loss: 5.1007, valid acc 0.5185\n",
      "epoch 366: train D loss: 0.6185, train F loss: 0.0219, trainF acc 0.9940, trainD acc 0.6551\n",
      "epoch 366: valid F loss: 5.7583, valid acc 0.4787\n",
      "epoch 367: train D loss: 0.6584, train F loss: 0.0952, trainF acc 0.9778, trainD acc 0.6340\n",
      "epoch 367: valid F loss: 6.0121, valid acc 0.4332\n",
      "epoch 368: train D loss: 0.6683, train F loss: 0.1730, trainF acc 0.9533, trainD acc 0.6095\n",
      "epoch 368: valid F loss: 5.5626, valid acc 0.4787\n",
      "epoch 369: train D loss: 0.6142, train F loss: 0.1158, trainF acc 0.9617, trainD acc 0.6583\n",
      "epoch 369: valid F loss: 4.4268, valid acc 0.5455\n",
      "epoch 370: train D loss: 0.6378, train F loss: 0.1659, trainF acc 0.9491, trainD acc 0.6508\n",
      "epoch 370: valid F loss: 4.7206, valid acc 0.4702\n",
      "epoch 371: train D loss: 0.6016, train F loss: 0.0744, trainF acc 0.9754, trainD acc 0.6927\n",
      "epoch 371: valid F loss: 4.5482, valid acc 0.5085\n",
      "epoch 372: train D loss: 0.6038, train F loss: 0.0596, trainF acc 0.9832, trainD acc 0.6736\n",
      "epoch 372: valid F loss: 4.8122, valid acc 0.5298\n",
      "epoch 373: train D loss: 0.6446, train F loss: 0.0376, trainF acc 0.9862, trainD acc 0.6358\n",
      "epoch 373: valid F loss: 4.7609, valid acc 0.5483\n",
      "epoch 374: train D loss: 0.6403, train F loss: 0.0314, trainF acc 0.9910, trainD acc 0.6378\n",
      "epoch 374: valid F loss: 5.0006, valid acc 0.5412\n",
      "epoch 375: train D loss: 0.6428, train F loss: 0.0173, trainF acc 0.9946, trainD acc 0.6280\n",
      "epoch 375: valid F loss: 5.0518, valid acc 0.5426\n",
      "epoch 376: train D loss: 0.6128, train F loss: 0.0282, trainF acc 0.9886, trainD acc 0.6618\n",
      "epoch 376: valid F loss: 4.7415, valid acc 0.5526\n",
      "epoch 377: train D loss: 0.6128, train F loss: 0.0257, trainF acc 0.9898, trainD acc 0.6635\n",
      "epoch 377: valid F loss: 4.7232, valid acc 0.5611\n",
      "epoch 378: train D loss: 0.6597, train F loss: 0.0212, trainF acc 0.9916, trainD acc 0.6225\n",
      "epoch 378: valid F loss: 4.4120, valid acc 0.5582\n",
      "epoch 379: train D loss: 0.6465, train F loss: 0.0296, trainF acc 0.9904, trainD acc 0.6346\n",
      "epoch 379: valid F loss: 4.6643, valid acc 0.5625\n",
      "epoch 380: train D loss: 0.5980, train F loss: 0.0481, trainF acc 0.9838, trainD acc 0.6698\n",
      "epoch 380: valid F loss: 5.1473, valid acc 0.5341\n",
      "epoch 381: train D loss: 0.6467, train F loss: 0.0954, trainF acc 0.9737, trainD acc 0.6464\n",
      "epoch 381: valid F loss: 4.7817, valid acc 0.4830\n",
      "epoch 382: train D loss: 0.7127, train F loss: 0.0499, trainF acc 0.9850, trainD acc 0.5638\n",
      "epoch 382: valid F loss: 5.4724, valid acc 0.5170\n",
      "epoch 383: train D loss: 0.6262, train F loss: 0.0316, trainF acc 0.9916, trainD acc 0.6488\n",
      "epoch 383: valid F loss: 5.5811, valid acc 0.5043\n",
      "epoch 384: train D loss: 0.5285, train F loss: 0.0199, trainF acc 0.9928, trainD acc 0.7548\n",
      "epoch 384: valid F loss: 5.9614, valid acc 0.5426\n",
      "epoch 385: train D loss: 0.5480, train F loss: 0.0202, trainF acc 0.9958, trainD acc 0.7207\n",
      "epoch 385: valid F loss: 6.2411, valid acc 0.5426\n",
      "epoch 386: train D loss: 0.7171, train F loss: 0.0238, trainF acc 0.9922, trainD acc 0.5841\n",
      "epoch 386: valid F loss: 5.9244, valid acc 0.5483\n",
      "epoch 387: train D loss: 0.6851, train F loss: 0.0221, trainF acc 0.9928, trainD acc 0.5685\n",
      "epoch 387: valid F loss: 5.0806, valid acc 0.5426\n",
      "epoch 388: train D loss: 0.6083, train F loss: 0.0212, trainF acc 0.9940, trainD acc 0.6606\n",
      "epoch 388: valid F loss: 4.8510, valid acc 0.5440\n",
      "epoch 389: train D loss: 0.6318, train F loss: 0.0187, trainF acc 0.9940, trainD acc 0.6485\n",
      "epoch 389: valid F loss: 4.8034, valid acc 0.5341\n",
      "epoch 390: train D loss: 0.6993, train F loss: 0.0592, trainF acc 0.9820, trainD acc 0.5832\n",
      "epoch 390: valid F loss: 5.4266, valid acc 0.5227\n",
      "epoch 391: train D loss: 0.6696, train F loss: 0.0291, trainF acc 0.9904, trainD acc 0.6095\n",
      "epoch 391: valid F loss: 5.0400, valid acc 0.5426\n",
      "epoch 392: train D loss: 0.6020, train F loss: 0.0775, trainF acc 0.9928, trainD acc 0.6774\n",
      "epoch 392: valid F loss: 5.3216, valid acc 0.5469\n",
      "epoch 393: train D loss: 0.6191, train F loss: 0.2699, trainF acc 0.9275, trainD acc 0.6502\n",
      "epoch 393: valid F loss: 3.3433, valid acc 0.4957\n",
      "epoch 394: train D loss: 0.6423, train F loss: 0.1605, trainF acc 0.9587, trainD acc 0.6288\n",
      "epoch 394: valid F loss: 3.1092, valid acc 0.5540\n",
      "epoch 395: train D loss: 0.6489, train F loss: 0.2306, trainF acc 0.9305, trainD acc 0.6329\n",
      "epoch 395: valid F loss: 2.7563, valid acc 0.5142\n",
      "epoch 396: train D loss: 0.6914, train F loss: 0.1617, trainF acc 0.9509, trainD acc 0.5916\n",
      "epoch 396: valid F loss: 2.5041, valid acc 0.5213\n",
      "epoch 397: train D loss: 0.7019, train F loss: 0.0737, trainF acc 0.9749, trainD acc 0.5630\n",
      "epoch 397: valid F loss: 3.7612, valid acc 0.5426\n",
      "epoch 398: train D loss: 0.6721, train F loss: 0.0485, trainF acc 0.9814, trainD acc 0.5800\n",
      "epoch 398: valid F loss: 5.2025, valid acc 0.5426\n",
      "epoch 399: train D loss: 0.6574, train F loss: 0.0475, trainF acc 0.9838, trainD acc 0.6118\n",
      "epoch 399: valid F loss: 4.6002, valid acc 0.5582\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "save_path = 'work/model/DaNNAdaptivateLambdaGamma1'\n",
    "best_acc = 0.0\n",
    "valid_acc = 0.0\n",
    "loss_record = {'trainF': {'loss': [], 'iter': []}, 'trainD': {'loss': [], 'iter': []}, 'valid': {'loss': [], 'iter': []}}   # for recording loss\n",
    "acc_record = {'trainF': {'acc': [], 'iter': []}, 'trainD': {'acc': [], 'iter': []}, 'valid': {'acc': [], 'iter': []}}      # for recording accuracy\n",
    "it = 0\n",
    "\n",
    "# 训练\n",
    "for epoch in range(nepoch):\n",
    "    # TODO：lamb = \n",
    "    p = epoch / nepoch\n",
    "    gamma = 1\n",
    "    lamb = 2 / (1 + math.exp(-gamma * p)) - 1\n",
    "    train_D_loss, train_F_loss, trainF_acc, trainD_acc = train_dann(source_dataloader, target_dataloader, lamb=lamb)\n",
    "\n",
    "    # log for train curve\n",
    "    loss_record['trainF']['loss'].append(train_F_loss)\n",
    "    loss_record['trainF']['iter'].append(it)\n",
    "    loss_record['trainD']['loss'].append(train_D_loss)\n",
    "    loss_record['trainD']['iter'].append(it)\n",
    "    acc_record['trainF']['acc'].append(trainF_acc)\n",
    "    acc_record['trainF']['iter'].append(it)\n",
    "    acc_record['trainD']['acc'].append(trainD_acc)\n",
    "    acc_record['trainD']['iter'].append(it)\n",
    "    print('epoch {:>3d}: train D loss: {:6.4f}, train F loss: {:6.4f}, trainF acc {:6.4f}, trainD acc {:6.4f}'.format(epoch, train_D_loss, train_F_loss, trainF_acc, trainD_acc))\n",
    "\n",
    "    valid_acc_manager = paddle.metric.Accuracy()\n",
    "    valid_F_loss = valid_epoch(valid_dataloader, valid_acc_manager)\n",
    "    valid_acc = valid_acc_manager.accumulate()\n",
    "    # log for valid curve\n",
    "    loss_record['valid']['loss'].append(valid_F_loss)\n",
    "    loss_record['valid']['iter'].append(it)\n",
    "    acc_record['valid']['acc'].append(valid_acc)\n",
    "    acc_record['valid']['iter'].append(it)\n",
    "    print('epoch {:>3d}: valid F loss: {:6.4f}, valid acc {:6.4f}'.format(epoch, valid_F_loss, valid_acc))\n",
    "\n",
    "    it += 1\n",
    "    \n",
    "    if valid_acc > best_acc:\n",
    "        best_acc = valid_acc\n",
    "        paddle.save(feature_extractor.state_dict(), os.path.join(save_path, 'extractor_model.pdparams'))\n",
    "        paddle.save(optimizer_F.state_dict(), os.path.join(save_path, 'extractor_optim.pdopt'))\n",
    "        paddle.save(label_predictor.state_dict(), os.path.join(save_path, 'predictor_model.pdparams'))\n",
    "        paddle.save(optimizer_C.state_dict(), os.path.join(save_path, 'predictor_optim.pdopt'))\n",
    "        paddle.save(domain_classifier.state_dict(), os.path.join(save_path, 'discriminator_model.pdparams'))\n",
    "        paddle.save(optimizer_D.state_dict(), os.path.join(save_path, 'discriminator_optim.pdopt'))\n",
    "    # 保存最后一组参数\n",
    "    if epoch == nepoch - 1:\n",
    "        paddle.save(feature_extractor.state_dict(), os.path.join(save_path, 'extractor_model_last.pdparams'))\n",
    "        paddle.save(optimizer_F.state_dict(), os.path.join(save_path, 'extractor_optim_last.pdopt'))\n",
    "        paddle.save(label_predictor.state_dict(), os.path.join(save_path, 'predictor_model_last.pdparams'))\n",
    "        paddle.save(optimizer_C.state_dict(), os.path.join(save_path, 'predictor_optim_last.pdopt'))\n",
    "        paddle.save(domain_classifier.state_dict(), os.path.join(save_path, 'discriminator_model_last.pdparams'))\n",
    "        paddle.save(optimizer_D.state_dict(), os.path.join(save_path, 'discriminator_optim_last.pdopt'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train VADA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# TODO \n",
    "# conditional entropy loss\n",
    "condition_criterion = None\n",
    "# TODO \n",
    "# vat\n",
    "vat_criterion = None\n",
    "\n",
    "def train_vada(source_dataloader, target_dataloader, lamb):\n",
    "    '''\n",
    "      Args:\n",
    "        source_dataloader: source data的dataloader\n",
    "        target_dataloader: target data的dataloader\n",
    "        lamb: 调控adversarial的loss系数。\n",
    "    '''\n",
    "    # D loss: Domain Classifier的loss\n",
    "    # F loss: Feature Extrator & Label Predictor的loss\n",
    "    running_D_loss, running_F_loss = 0.0, 0.0\n",
    "    F_hit, F_num = 0.0, 0.0\n",
    "    D_hit, D_num = 0.0, 0.0\n",
    "\n",
    "    for i, ((source_data, source_label), (target_data, _)) in enumerate(zip(source_dataloader, target_dataloader)):\n",
    "        \n",
    "        # 我们把source data和target data混在一起，否则batch_norm可能会算错 (两边的data的mean/var不太一样)\n",
    "        mixed_data = paddle.concat([source_data, target_data], axis=0)\n",
    "        domain_label = paddle.zeros([source_data.shape[0] + target_data.shape[0], 1])\n",
    "        # 设定source data的label为1\n",
    "        domain_label[:source_data.shape[0]] = 1\n",
    "\n",
    "        # Step 1 : 训练Domain Classifier\n",
    "        feature = feature_extractor(mixed_data)\n",
    "        # 因为我们在Step 1不需要训练Feature Extractor，所以把feature做detach阶段梯度避免loss反向传播后更新Feature Extractor的参数。\n",
    "        domain_logits = domain_classifier(feature.detach())\n",
    "        loss = domain_criterion(domain_logits, domain_label)\n",
    "        running_D_loss+= loss.numpy()[0]\n",
    "        loss.backward()\n",
    "        optimizer_D.step()\n",
    "        \n",
    "        domain_pred = 1*(F.sigmoid(domain_logits).numpy()>0.5).astype('int64')\n",
    "        D_hit += np.sum(domain_pred == domain_label.numpy())\n",
    "        D_num += mixed_data.shape[0]\n",
    "\n",
    "        # Step 2 : 训练Feature Extractor和Label Predictor\n",
    "        class_logits = label_predictor(feature[:source_data.shape[0]])\n",
    "        domain_logits = domain_classifier(feature)\n",
    "        loss = class_criterion(class_logits, source_label)\n",
    "        running_F_loss+= loss.numpy()[0]\n",
    "        loss -= lamb * domain_criterion(domain_logits, domain_label)\n",
    "\n",
    "        # TOOD\n",
    "        # conditional entropy loss.\n",
    "        trg_class_logits = label_predictor(feature[source_data.shape[0]:])\n",
    "        loss_trg_cent = \n",
    "        \n",
    "        # TOOD\n",
    "        # virtual adversarial loss.\n",
    "        loss_src_vat = \n",
    "        loss_trg_vat = \n",
    "\n",
    "        loss += 1 * loss_src_vat + 0.01 * (loss_trg_vat + loss_trg_cent)\n",
    "\n",
    "        loss.backward()\n",
    "        optimizer_F.step()\n",
    "        optimizer_C.step()\n",
    "\n",
    "        optimizer_D.clear_grad()\n",
    "        optimizer_F.clear_grad()\n",
    "        optimizer_C.clear_grad()\n",
    "        \n",
    "        F_hit += np.sum((paddle.argmax(class_logits, axis=1) == source_label).numpy())\n",
    "        F_num += source_data.shape[0]\n",
    "        print(i, end='\\r')\n",
    "\n",
    "    return running_D_loss / (i+1), running_F_loss / (i+1), F_hit / F_num, D_hit / D_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 训练 同 DANN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_learning_curve(record, title='loss', ylabel='Loss'):\n",
    "    ''' Plot learning curve '''\n",
    "    color_dict = {'trainF': 'tab:green', 'trainD': 'tab:blue', 'valid': 'tab:red'}\n",
    "    ymax = 0.0\n",
    "    for key in record.keys():\n",
    "        mmax = max(map(float, record[key][title]))\n",
    "        if ymax < mmax:\n",
    "            ymax = mmax\n",
    "    ymax *= 1.1\n",
    "    \n",
    "    ymin = 1e6\n",
    "    for key in record.keys():\n",
    "        mmin = min(map(float, record[key][title]))\n",
    "        if ymin > mmin:\n",
    "            ymin = mmin\n",
    "    ymin *= 0.9\n",
    "    \n",
    "    # total_steps = nepoch\n",
    "    figure(figsize=(10, 6))\n",
    "    for key in record.keys():\n",
    "        x = list(map(int, record[key]['iter']))\n",
    "        plt.plot(x, record[key][title], c=color_dict[key], label=key)\n",
    "\n",
    "    plt.ylim(ymin, ymax)\n",
    "    plt.xlabel('Training steps')\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.title('Learning curve of {}'.format(title))\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve(loss_record, title='loss', ylabel='Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve(acc_record, title='acc', ylabel='Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Public test set of inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# save_path = 'work/model/SourceOnly'\n",
    "# save_path = 'work/model/DaNN'\n",
    "# save_path = 'work/model/DaNNAdaptivateLambda'\n",
    "save_path = 'work/model/DaNNAdaptivateLambdaGamma1'\n",
    "predictions = []\n",
    "extractor_state_dict = paddle.load(os.path.join(save_path, 'extractor_model.pdparams'))\n",
    "predictor_state_dict = paddle.load(os.path.join(save_path, 'predictor_model.pdparams'))\n",
    "\n",
    "extractor = FeatureExtractor()\n",
    "predictor = LabelPredictor()\n",
    "\n",
    "extractor.set_state_dict(extractor_state_dict)\n",
    "predictor.set_state_dict(predictor_state_dict)\n",
    "\n",
    "extractor.eval()\n",
    "predictor.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation loss is: 4.664308547973633, validation acc is: 0.5625\n"
     ]
    }
   ],
   "source": [
    "valid_loss = 0.0\n",
    "valid_acc_manager = paddle.metric.Accuracy()\n",
    "\n",
    "for batch_id, data in enumerate(valid_dataloader):\n",
    "    x_data, y_data = data\n",
    "    labels = paddle.unsqueeze(y_data, axis=1)\n",
    "    \n",
    "    # ===================forward=====================\n",
    "    # We don't need gradient in testing.\n",
    "    # Using paddle.no_grad() accelerates the forward process.\n",
    "    with paddle.no_grad():\n",
    "        feat = extractor(x_data)\n",
    "        predicts = predictor(feat)\n",
    "        loss = class_criterion(predicts, y_data)\n",
    "\n",
    "    # ==================calculate acc================\n",
    "    acc = valid_acc_manager.compute(predicts, labels)\n",
    "    valid_acc_manager.update(acc)\n",
    "\n",
    "    valid_loss += loss\n",
    "\n",
    "valid_acc = valid_acc_manager.accumulate()\n",
    "total_valid_loss = valid_loss / (batch_id+1)\n",
    "print(\"validation loss is: {}, validation acc is: {}\".format(total_valid_loss.numpy(), valid_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization\n",
    "We use t-SNE plot to observe the distribution of extracted features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "def sample_source(num):\n",
    "    classes = source_dataset.class_to_idx\n",
    "    samples = np.array(source_dataset.samples)\n",
    "    labels = samples[:, 1].astype(\"int64\")\n",
    "    paths = []\n",
    "    for cid in range(7):\n",
    "        subsamples = samples[labels == cid]\n",
    "        paths.extend(np.random.choice(subsamples[:, 0], size=num, replace=False))\n",
    "    return paths\n",
    "\n",
    "def sample_target(num):\n",
    "    classes = target_dataset.class_to_idx\n",
    "    samples = np.array(target_dataset.samples)\n",
    "    np.random.shuffle(samples)\n",
    "    subsamples = samples[:num]\n",
    "    labels = subsamples[:, 1].astype(\"int64\")\n",
    "    paths = subsamples[:, 0]\n",
    "    return paths\n",
    "\n",
    "def minmax_norm(x):\n",
    "    mmin = np.min(x, axis=0)\n",
    "    mmax = np.max(x, axis=0)\n",
    "    return (x - mmin) / (mmax - mmin)\n",
    "\n",
    "\n",
    "def draw_tsne(feat1, feat2):\n",
    "    tsne = TSNE(n_components=2, random_state=0, learning_rate=200, perplexity=18, n_iter=5000, init='random')\n",
    "    seg = len(feat1)\n",
    "    X = tsne.fit_transform(np.concatenate((feat1, feat2)))\n",
    "    X = minmax_norm(X)\n",
    "    figure(figsize=(8, 8), frameon=False)\n",
    "\n",
    "    plt.scatter(X[:seg, 0], X[:seg, 1], c=\"blue\", s=10, marker=\"o\")\n",
    "    plt.scatter(X[seg:, 0], X[seg:, 1], c=\"red\", s=10, marker=\"o\")\n",
    "\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "source_paths = sample_source(num=30)\n",
    "source_imgs = []\n",
    "for path in source_paths:\n",
    "    source_imgs.append(source_transform(default_loader(path)))\n",
    "\n",
    "\n",
    "target_paths = sample_target(num=210)\n",
    "target_imgs = []\n",
    "for path in target_paths:\n",
    "    target_imgs.append(target_transform(default_loader(path)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "source_feats = []\n",
    "for img in source_imgs:\n",
    "    img = img.unsqueeze(0)\n",
    "    source_feats.append(extractor(img).numpy())\n",
    "source_feats = np.array(source_feats)\n",
    "\n",
    "target_feats = []\n",
    "for img in target_imgs:\n",
    "    img = img.unsqueeze(0)\n",
    "    target_feats.append(extractor(img).numpy())\n",
    "target_feats = np.array(target_feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\anaconda3\\envs\\paddle_cuda\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:1164: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_tsne(source_feats, target_feats) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Private test set of inference\n",
    "Notice that you should submit the csv file generated here for private test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Iterate the evaluation set by batches.\n",
    "# test on evaluation set\n",
    "for batch_id, data in enumerate(test_dataloader):\n",
    "    x_data, y_data = data\n",
    "    # We don't need gradient in testing.\n",
    "    # Using paddle.no_grad() accelerates the forward process.\n",
    "    with paddle.no_grad():\n",
    "        feat = extractor(x_data)\n",
    "        logits = predictor(feat)\n",
    "    \n",
    "    predictions.extend(paddle.argmax(logits, axis=1).cpu().numpy().tolist())\n",
    "    \n",
    "# Save predictions into the file.\n",
    "with open(\"predict.csv\", \"w\") as f:\n",
    "\n",
    "    # The first row must be \"Id, Category\"\n",
    "    f.write(\"Id,Category\\n\")\n",
    "\n",
    "    # For the rest of the rows, each image id corresponds to a predicted class.\n",
    "    for i, pred in enumerate(predictions):\n",
    "         f.write(f\"{i},{pred}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "np.float64(0.3363755443363323)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def adjust_lambda(p, gamma):\n",
    "    return 2 / (1 + np.exp(-gamma * p)) - 1\n",
    "\n",
    "p_values = np.linspace(0, 1, 100)\n",
    "gamma_values = [0.5, 1, 2, 5, 10]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "for gamma in gamma_values:\n",
    "    lambda_values = adjust_lambda(p_values, gamma)\n",
    "    plt.plot(p_values, lambda_values, label=f'gamma={gamma}')\n",
    "\n",
    "plt.xlabel('x (p)')\n",
    "plt.ylabel('Lambda ')\n",
    "plt.title('diff gammas Lambda curve')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "adjust_lambda(1,0.7)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "paddle_cuda",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
